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WO2025193585A1 - Score de fréquence cardiaque pour défibrillateurs cardioverteurs portables - Google Patents

Score de fréquence cardiaque pour défibrillateurs cardioverteurs portables

Info

Publication number
WO2025193585A1
WO2025193585A1 PCT/US2025/019128 US2025019128W WO2025193585A1 WO 2025193585 A1 WO2025193585 A1 WO 2025193585A1 US 2025019128 W US2025019128 W US 2025019128W WO 2025193585 A1 WO2025193585 A1 WO 2025193585A1
Authority
WO
WIPO (PCT)
Prior art keywords
heart rate
patient
score
arrhythmia
initial
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2025/019128
Other languages
English (en)
Inventor
Evan Z. BURLEW
Joshua M. LAMIANO
Pai LI
Francesco Nicolo
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zoll Medical Corp
Original Assignee
Zoll Medical Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zoll Medical Corp filed Critical Zoll Medical Corp
Publication of WO2025193585A1 publication Critical patent/WO2025193585A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3904—External heart defibrillators [EHD]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02438—Measuring pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/361—Detecting fibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/363—Detecting tachycardia or bradycardia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0408—Use-related aspects
    • A61N1/046—Specially adapted for shock therapy, e.g. defibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0472—Structure-related aspects
    • A61N1/0484—Garment electrodes worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3925—Monitoring; Protecting

Definitions

  • the present disclosure relates to a wearable cardiac treatment system configured to treat cardiac arrhythmias occurring in ambulatory patients.
  • Heart failure if left untreated, can lead to certain life-threatening arrhythmias. Both atrial and ventricular arrhythmias are common in patients with heart failure. One of the deadliest cardiac arrhythmias is ventricular fibrillation, which occurs when normal, regular electrical impulses are replaced by irregular and rapid impulses, causing the heart muscle to stop normal contractions. Because the victim has no perceptible warning of the impending fibrillation, death often occurs before the necessary medical assistance can arrive. Other cardiac arrhythmias can include excessively slow heart rates known as bradycardia or excessively fast heart rates known as tachycardia.
  • Cardiac arrest can occur when a patient in which various arrhythmias of the heart, such as ventricular fibrillation, ventricular tachycardia, pulseless electrical activity (PEA), and/or asystole (heart stops all electrical activity), result in the heart providing insufficient levels of blood flow- to the brain and other vital organs for the support of life. It is generally useful to monitor heart failure patients to assess heart failure symptoms early and provide interventional therapies as soon as possible.
  • various arrhythmias of the heart such as ventricular fibrillation, ventricular tachycardia, pulseless electrical activity (PEA), and/or asystole (heart stops all electrical activity)
  • Cardiac treatment devices may provide defibrillation shocks to the patient if an abnormal cardiac rhythm is detected.
  • the abnormal cardiac rhythm is detected using electrocardiogram (ECG) electrodes, and the defibrillation shocks are provided using therapy electrodes.
  • ECG electrocardiogram
  • a wearable cardiac defibrillator with improved heart rate detection capability for arrhythmia warnings and treatment includes a plurality of sensing electrodes configured to contact a patient’s skin and sense electrical cardiac activity of the patient, a plurality of therapy electrodes configured to deliver therapeutic shocks to a heart of the patient, and a cardiac controller configured to be operably connected to the plurality of sensing electrodes and the plurality of therapy electrodes.
  • the cardiac controller is configured to perform an initial determination of an arrhythmia condition occurring in the patient based on an arrhythmia analysis of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity, identify and record an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generate an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • ECG electrocardiogram
  • the cardiac controller is configured to determine a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generate an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, compare the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determine whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • Implementations of the wearable cardiac defibrillator can include one or more of the following features.
  • One or more of the plurality of sensing electrodes and/or the plurality of therapy electrodes is provided on one or more adhesive patches configured to be removably applied to the patient’s skin.
  • the wearable cardiac defibrillator further includes a garment configured to be worn about a torso of the patient.
  • the garment is configured to support the plurality of sensing electrodes and the plurality of therapy electrodes.
  • the garment is configured to support the cardiac controller.
  • One or more of the plurality of sensing electrodes and/or the plurality of therapy electrodes is provided on one or more adhesive patches configured to be removably applied to the patient’s skin.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity includes a heart rate analysis of the initial heart rate of the patient.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity further includes at least one of a morphological analysis of the ECG signals or a frequency analysis of the ECG signals.
  • the first level heart rate value includes a predetermined heart rate value.
  • the first level heart rate value is between 60 and 120 bpm.
  • the first level heart rate value is between 130 and 200 bpm.
  • the first level heart rate value includes a user-configurable heart rate value.
  • the user-configurable heart rate value corresponds to a user-configurable threshold for detecting one or more types of arrhythmia conditions.
  • the second level heart rate range is configured to be between the first level heart rate value and a second level heart rate value.
  • the cardiac controller is further configured to determine the initial heart rate score for the initial heart rate by identifying a third level heart rate range configured to be above the second level heart rate value and determining the initial heart rate score based on whether the onset heart rate of the patient is within the first level heart rate range, the second level heart rate range, or the third level heart rate range.
  • the first level heart rate value includes a predetermined heart rate value.
  • the first level heart rate value includes a user-configurable heart rate value.
  • the second level heart rate value includes a predetermined heart rate value.
  • the second level heart rate value includes a user-configurable heart rate value.
  • Determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range, the second level heart rate range, or the third level heart rate range includes assigning a first heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the first level heart rate range, assigning a second heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the second level heart rate range, where the second heart rate score amount is greater than the first heart rate score amount, or assigning a third heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the third level heart rate range, where the third heart rate score amount is greater than the second heart rate score amount.
  • Determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range includes assigning a first heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the first level heart rate range or assigning a second heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the second level heart rate range, wherein the second heart rate score amount is greater than the first heart rate score amount.
  • the cardiac controller is further configured to compare the initial heart rate score to the predetermined heart rate score threshold and generate the arrhythmia verification heart rate score if the initial heart rate score does not transgress the predetermined heart rate score threshold.
  • the cardiac controller is configured to generate the arrhythmia verification heart rate score by calculating a median heart rate of a plurality of past heart rates, the plurality of past heart rates including the second heart rate and generating a median heart rate score for the median heart rate, wherein the arrhythmia verification heart rate score is at least partially based on the median heart rate score.
  • the plurality of past heart rates includes a predetermined number of consecutive past heart rates. The predetermined number of consecutive past heart rates is between 2 and 200. The predetermined number of consecutive past heart rates is between 5 and 20. The plurality of past heart rates includes the second heart rate. The plurality of past heart rates further includes the initial heart rate.
  • the cardiac controller is further configured to determine a third heart rate subsequent to the initial heart rate and the second heart rate of the patient for another period of time following the initial determination of the arrhythmia condition.
  • the arrhythmia verification heart rate score is a first arrhythmia verification heart rate score.
  • the cardiac controller is configured to generate a second arrhythmia verification heart rate score using the initial heart rate score and the third heart rate in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the cardiac controller is configured to generate the second arrhythmia verification heart rate score using the initial heart rate score and the third heart rate.
  • the cardiac controller is configured to compare the second arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the cardiac controller is configured to initiate the treatment sequence in response to determining that the second arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the cardiac controller is configured to generate a third arrhythmia verification score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the cardiac controller is further configured to determine the initial heart rate of the patient and the second heart rate of the patient based on detected R peaks in the ECG signals.
  • the cardiac controller is further configured to detect the R peaks in the ECG signals using a separation threshold configured to separate R peaks from other portions of the ECG signals.
  • the cardiac controller is configured to set the separation threshold by using local maxima of the ECG signals.
  • Using the local maxima in the ECG signals to set the separation threshold includes identifying a plurality of local maxima in the ECG signals and applying one or more clustering analyses to the identified plurality of local maxima to classify each of the identified plurality of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals.
  • Using the local maxima in the ECG signals to set the separation threshold further includes calculating a margin between the classified one or more true peaks and the classified one or more false peaks and determining whether the calculated margin transgresses a minimum margin condition. Calculating the margin between the classified one or more true peaks and the classified one or more false peaks includes normalizing the margin.
  • the cardiac controller is configured to perform one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition.
  • Using the local maxima in the ECG signals to set the separation threshold further includes determining whether the calculated margin transgresses a maximum margin condition.
  • the cardiac controller is configured to perform one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the calculated margin does not transgress the maximum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the calculated margin transgresses the maximum margin condition.
  • Using the local maxima in the ECG signals to set the separation threshold further includes counting a number of the classified one or more true peaks and determining whether the number of the classified one or more true peaks is greater than a predetermined true peak number.
  • Using the local maxima in the ECG signals to set the separation threshold further includes performing one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the number of the classified one or more true peaks is greater than the predetermined true peak number or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the number of the classified one or more true peaks is not greater than the predetermined true peak number.
  • Determining the updated separation threshold includes calculating an average between an amplitude of a smallest classified true peak and an amplitude of a largest classified false peak.
  • the cardiac controller is configured to determine the initial heart rate of the patient and the second heart rate of the patient by sorting a plurality of ECG intervals of the ECG signals into a plurality of histogram bins.
  • the plurality of ECG intervals are based on the detected R peaks in the ECG signals.
  • the plurality of ECG intervals includes a plurality of intervals between a plurality of the detected R peaks in the ECG signals.
  • the cardiac controller is further configured to determine the initial heart rate of the patient and the second heart rate of the patient by determining which histogram bin of the plurality of histogram bins has a greatest magnitude and estimating the respective heart rate of the patient using the histogram bin having the greatest magnitude.
  • the cardiac controller is further configured to determine the initial heart rate of the patient and the second heart rate of the patient by determining that a histogram bin having a greatest magnitude of the plurality of histogram bins is a guard histogram bin and using a default method of determining heart rate in response to determining that the histogram bin having the greatest magnitude is the guard histogram bin.
  • Pairs of the sensing electrodes form a plurality of sensing leads.
  • the cardiac controller is further configured to sense the electrical cardiac acti vity of the patient using the plurality of sensing leads and determine ongoing heart rates of the patient using the ECG signals generated via a preferred sensing lead from the plurality of sensing leads.
  • the cardiac controller is further configured to determine at least one test heart rate for each of the plurality of sensing leads using the ECG signals generated via each respective lead and detect whether the test heart rates for the plurality of sensing leads agree with each other.
  • the cardiac controller is further configured to determine, based on whether the test heart rates for the plurality of sensing leads agree with each other, whether to maintain determining the ongoing heart rates of the patient using the ECG signals generated via the preferred sensing lead.
  • the cardiac controller is further configured to maintain determining the ongoing heart rates of the patient using the ECG signals generated via the preferred sensing lead in response to the test heart rates for the plurality of sensing leads agreeing with each other.
  • the cardiac controller is further configured to switch to determining the ongoing heart rates of the patient using the ECG signals generated via a non-preferred lead of the plurality of sensing leads in response to detecting that the at least one test heart rate for the preferred sensing lead is a predetermined amount above the at least one test heart rate for each non-preferred lead of the plurality of sensing leads.
  • the cardiac controller is further configured to switch to determining the ongoing heart rates of the patient using the ECG signals generated via a non-preferred lead of the plurality of sensing leads in response to detecting that the at least one test heart rate for the preferred sensing lead transgresses a predetermined sensing threshold and the at least one test heart rate for each nonpreferred lead of the plurality of sensing leads does not transgress the predetermined sensing threshold.
  • the ongoing heart rates of the patient include the initial heart rate and the second heart rate.
  • a method for improving heart rate detection capability for arrhythmia warnings and treatment by a wearable cardiac defibrillator includes sensing electrical cardiac activity of a patient by a plurality of sensing electrodes, performing an initial determination of an arrhythmia condition occurring in a patient based on an arrhythmia analysis of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity, identifying and recording an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generating an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • ECG electrocardiogram
  • the method includes determining a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generating an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, comparing the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determining whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • Implementations of the method for improving heart rate detection capability for arrhythmia warnings and treatment by a wearable cardiac defibrillator can include one or more of the following features.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity includes a heart rate analysis of the initial heart rate of the patient.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity further includes at least one of a morphological analysis of the ECG signals or a frequency analysis of the ECG signals.
  • Generating the arrhythmia verification heart rate score includes calculating a median heart rate of a plurality of past heart rates, the plurality of past heart rates including the second heart rate and generating a median heart rate score for the median heart rate.
  • the arrhythmia verification heart rate score is at least partially based on the median heart rate score.
  • the method includes initiating the treatment sequence in response to determining that the arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the arrhythmia verification heart rate score is a first arrhythmia verification heart rate score.
  • the method further includes generating a second arrhythmia verification heart rate score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the method includes comparing the second arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the method includes initiating the treatment sequence in response to determining that the second arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the method includes generating a third arrhythmia verification score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the method includes determining the initial heart rate of the patient. Determining the initial heart rate of the patient and determining the second heart rate include detecting R peaks in the ECG signals. Detecting the R peaks in the ECG signals includes using a separation threshold configured to separate R peaks from other portions of the ECG signals. The method includes setting the separation threshold using local maxima of the ECG signals.
  • Setting the separation threshold using the local maxima in the ECG signals includes identifying a plurality of local maxima in the ECG signals and applying one or more clustering analyses to the identified plurality of local maxima to classify each of the identified plurality of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals.
  • Setting the separation threshold using the local maxima in the ECG signals further includes calculating a margin between the classified one or more true peaks and the classified one or more false peaks and determining the separation threshold based on whether the calculated margin transgresses a minimum margin condition.
  • Sensing the electrical cardiac activity of the patient by the plurality of sensing electrodes includes sensing the electrical cardiac activity of the patient using a plurality 7 of sensing leads, with pairs of the sensing electrodes forming the plurality of sensing leads.
  • the method includes determining ongoing heart rates of the patient using the ECG signals generated via a preferred sensing lead from the plurality of sensing leads.
  • the method includes determining at least one test heart rate for each of the plurality of sensing leads using the ECG signals generated via each respective lead and detecting whether the test heart rates for the plurality of sensing leads agree with each other.
  • a non-transitory computer-readable medium stores sequences of instructions executable by at least one processor, the sequences of instructions instructing the at least one processor to improve heart rate detection capability for arrhythmia warnings and treatment by a wearable cardiac defibrillator.
  • the sequences of instructions include instructions to sense electrical cardiac activity of a patient by a plurality of sensing electrodes, perform an initial determination of an arrhythmia condition occurring in a patient based on an arrhythmia analysis of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity, identify and record an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generate an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • ECG electrocardiogram
  • the sequences of instructions include instructions to determine a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generate an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, compare the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determine whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • Implementations of the non-transitory computer-readable medium can include one or more of the following features.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity includes a heart rate analysis of the initial heart rate of the patient.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity further includes at least one of a morphological analysis of the ECG signals or a frequency analysis of the ECG signals.
  • the first level heart rate value includes a predetermined heart rate value.
  • the first level heart rate value includes a user-configurable heart rate value.
  • the second level heart rate range is configured to be between the first level heart rate value and a second level heart rate value.
  • the instructions to determine the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range include instructions to assign a first heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the first level heart rate range or assign a second heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the second level heart rate range, wherein the second heart rate score amount is greater than the first heart rate score amount.
  • the instructions to generate the arrhythmia verification heart rate score include instructions to calculate a median heart rate of a plurality of past heart rates, the plurality of past heart rates including the second heart rate and generate a median heart rate score for the median heart rate, wherein the arrhythmia verification heart rate score is at least partially based on the median heart rate score.
  • the sequences of instructions further include instructions to initiate the treatment sequence in response to determining that the arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the arrhythmia verification heart rate score is a first arrhythmia verification heart rate score.
  • the sequence of instructions further includes instructions to generate a second arrhythmia verification heart rate score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the sequences of instructions further include instructions to compare the second arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the sequences of instructions further include instructions to determine that the second arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the sequences of instructions further include instructions to generate a third arrhythmia verification score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the sequences of instructions further include instructions to determine the initial heart rate of the patient.
  • the instructions to determine the initial heart rate of the patient and determine the second heart rate include instructions to detect R peaks in the ECG signals.
  • the instructions to detect the R peaks in the ECG signals include instructions to use a separation threshold configured to separate R peaks from other portions of the ECG signals.
  • the sequences of instructions further include instructions to set the separation threshold using local maxima of the ECG signals.
  • the instructions to set the separation threshold using the local maxima in the ECG signals include instructions to identify a plurality of local maxima in the ECG signals and apply one or more clustering analyses to the identified plurality of local maxima to classify each of the identified plurality of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals.
  • the instructions to set the separation threshold using the local maxima in the ECG signals further include instructions to calculate a margin between the classified one or more true peaks and the classified one or more false peaks and determine the separation threshold based on whether the calculated margin transgresses a minimum margin condition.
  • the instructions to determine the initial heart rate of the patient and determine the second heart rate further include instructions to sort a plurality of ECG intervals of the ECG signals into a plurality of histogram bins. The plurality of ECG intervals are based on the detected R peaks in the ECG signals.
  • the instructions to determine the initial heart rate of the patient and determine the second heart rate further include instructions to determine which histogram bin of the plurality of histogram bins has a greatest magnitude and estimate the respective heart rate of the patient using the histogram bin having the greatest magnitude.
  • a wearable cardiac defibrillator with improved heart rate detection capability for arrhythmia warnings and treatment includes a plurality of sensing electrodes configured to contact a patient’s skin and sense electrical cardiac activity of the patient, a plurality of therapy electrodes configured to deliver therapeutic shocks to a heart of the patient, and a cardiac controller configured to be operably connected to the plurality of sensing electrodes and the plurality of therapy electrodes.
  • the cardiac controller is configured to set a separation threshold configured to separate R peaks from other portions of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity by identifying a plurality of local maxima in the ECG signals, applying one or more clustering analyses to the identified plurality 7 of local maxima to classify each of the identified plurality 7 of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals, calculating a margin between the classified one or more true peaks and the classified one or more false peaks, and determining the separation threshold based on whether the calculated margin transgresses a minimum margin condition.
  • the cardiac controller is configured to detect R peaks in the ECG signals using the separation threshold and determine a plurality’ of heart rates using the detected R peaks in the ECG signals.
  • Implementations of the wearable cardiac defibrillator can include one or more of the following features.
  • One or more of the plurality' of sensing electrodes and/or the plurality of therapy electrodes is provided on one or more adhesive patches configured to be removably applied to the patient’s skin.
  • the yvearable cardiac defibrillator includes a garment configured to be worn about a torso of the patient. The garment is configured to support the plurality of sensing electrodes and the plurality of therapy electrodes. The garment is configured to support the cardiac controller.
  • One or more of the plurality of sensing electrodes and/or the plurality' of therapy electrodes is provided on one or more adhesive patches configured to be removably applied to the patient’s skin.
  • Calculating the margin between the classified one or more true peaks and the classified one or more false peaks includes normalizing the margin.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition. Determining the separation threshold is further based on whether the calculated margin transgresses a maximum margin condition.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the calculated margin does not transgress the maximum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the calculated margin transgresses the maximum margin condition.
  • the cardiac controller is further configured to set the separation threshold configured to separate the R peaks from the other portions of the ECG signals by counting a number of the classified one or more true peaks. Determining the separation threshold is further based on whether the number of the classified one or more true peaks is greater than a predetermined true peak number.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the number of the classified one or more true peaks is greater than the predetermined true peak number or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the number of the classified one or more true peaks is not greater than the predetermined true peak number.
  • Determining the updated separation threshold includes calculating an average between an amplitude of a smallest classified true peak and an amplitude of a largest classified false peak.
  • the cardiac controller is further configured to perform an initial determination of an arrhythmia condition occurring in the patient based on an arrhythmia analysis of the ECG signals, identify and record, from the plurality of heart rates, an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generate an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • the cardiac controller is further configured to determine, as part of the plurality of heart rates, a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generate an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, compare the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determine whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity includes a heart rate analysis of the initial heart rate of the patient.
  • the arrhythmia analysis of the ECG signals generated from the sensed electrical cardiac activity further includes at least one of a morphological analysis of the ECG signals or a frequency analysis of the ECG signals.
  • the first level heart rate value includes a predetermined heart rate value.
  • the first level heart rate value includes a user-configurable heart rate value.
  • the second level heart rate range is configured to be between the first level heart rate value and a second level heart rate value.
  • the cardiac controller is configured to determine the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range by assigning a first heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the first level heart rate range or assigning a second heart rate score amount to the initial heart rate score on determining that the initial heart rate is within the second level heart rate range, wherein the second heart rate score amount is greater than the first heart rate score amount.
  • the cardiac controller is configured to generate the arrhythmia verification heart rate score by calculating a median heart rate of a plurality of past heart rates, the plurality of past heart rates including the second heart rate and generating a median heart rate score for the median heart rate, wherein the arrhythmia verification heart rate score is at least partially based on the median heart rate score.
  • the cardiac controller is further configured to initiate the treatment sequence in response to determining that the arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the arrhythmia verification heart rate score is a first arrhythmia verification heart rate score
  • the cardiac controller is further configured to generate a second arrhythmia verification heart rate score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the cardiac controller is further configured to compare the second arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the cardiac controller is further configured to initiate the treatment sequence in response to determining that the second arrhythmia verification heart rate score transgresses the predetermined heart rate score threshold.
  • the cardiac controller is further configured to generate a third arrhythmia verification score in response to determining that the first arrhythmia verification heart rate score does not transgress the predetermined heart rate score threshold.
  • the cardiac controller is configured to determine the plurality 7 of heart rates using the detected R peaks in the ECG signals by sorting a plurality of ECG intervals of the ECG signals into a plurality of histogram bins, wherein the plurality of ECG intervals are based on the detected R peaks in the ECG signals.
  • the cardiac controller is further configured to determine the plurality of heart rates using the detected R peaks in the ECG signals by determining which histogram bin of the plurality of histogram bins has a greatest magnitude and estimating the respective heart rate of the patient using the histogram bin having the greatest magnitude.
  • Pairs of the sensing electrodes form a plurality of sensing leads
  • the cardiac controller is further configured to sense the electrical cardiac activity of the patient using the plurality 7 of sensing leads and determine ongoing heart rates of the patient using the ECG signals generated via a preferred sensing lead from the plurality of sensing leads.
  • the cardiac controller is further configured to determine at least one test heart rate for each of the plurality of sensing leads using the ECG signals generated via each respective lead and detect whether the test heart rates for the plurality of sensing leads agree with each other.
  • the cardiac controller is further configured to determine, based on whether the test heart rates for the plurality of sensing leads agree with each other, whether to maintain determining the ongoing heart rates of the patient using the ECG signals generated via the preferred sensing lead.
  • a method for improved heart rate detection capability for arrhythmia warnings and treatment by a wearable cardiac defibrillator includes sensing electrical cardiac activity of a patient by a plurality of sensing electrodes and setting a separation threshold configured to separate R peaks from other portions of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity'.
  • ECG electrocardiogram
  • Setting the separation threshold includes identifying a plurality’ of local maxima in the ECG signals, applying one or more clustering analyses to the identified plurality of local maxima to classify each of the identified plurality of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals, calculating a margin between the classified one or more true peaks and the classified one or more false peaks, and determining the separation threshold based on whether the calculated margin transgresses a minimum margin condition.
  • the method includes detecting R peaks in the ECG signals using the separation threshold and determining a plurality of heart rates using the detected R peaks in the ECG signals.
  • Implementations of the method for improved heart rate detection capability for arrhythmia warnings and treatment by a wearable cardiac defibrillator can include one or more of the following features.
  • the method includes calculating the margin between the classified one or more true peaks and the classified one or more false peaks includes normalizing the margin.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the classified margin transgresses the minimum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition. Determining the separation threshold is further based on whether the calculated margin transgresses a maximum margin condition.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the calculated margin does not transgress the maximum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the calculated margin transgresses the maximum margin condition. Setting the separation threshold further includes counting a number of the classified one or more true peaks. Determining the separation threshold is further based on whether the number of the classified one or more true peaks is greater than a predetermined true peak number.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the number of the classified one or more true peaks is greater than the predetermined true peak number or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the number of the classified one or more true peaks is not greater than the predetermined true peak number.
  • Determining the updated separation threshold includes calculating an average between an amplitude of a smallest classified true peak and an amplitude of a largest classified false peak.
  • the method includes performing an initial determination of an arrhythmia condition occurring in the patient based on an arrhythmia analysis of the ECG signals, identifying and recording, from the plurality of heart rates, an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generating an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • the method includes determining, as part of the plurality of heart rates, a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generating an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, comparing the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determining whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • the sequences of instructions include instructions to sense electrical cardiac activity of a patient by a plurality of sensing electrodes, set a separation threshold configured to separate R peaks from other portions of electrocardiogram (ECG) signals generated from the sensed electrical cardiac activity by identifying a plurality of local maxima in the ECG signals, applying one or more clustering analyses to the identified plurality of local maxima to classify each of the identified plurality of local maxima as a true peak corresponding to a predicted R peak of the ECG signals or a false peak corresponding to a predicted other peak of the ECG signals, calculating a margin between the classified one or more true peaks and the classified one or more false peaks, and determining the separation threshold based on whether the calculated margin transgresses a minimum margin condition.
  • the sequences of instructions include instructions to detect R peaks in the ECG signals using the separation threshold and determine a plurality of heart rates using the detected R peaks in the ECG signals.
  • Implementations of the non-transitory computer-readable medium can include one or more of the following features.
  • Calculating the margin between the classified one or more true peaks and the classified one or more false peaks includes normalizing the margin.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the classified margin transgresses the minimum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition. Determining the separation threshold is further based on whether the calculated margin transgresses a maximum margin condition.
  • Determining the separation threshold includes one of determining an updated separation threshold based on amplitudes of the classified one or more true peaks if the calculated margin transgresses the minimum margin condition and if the calculated margin does not transgress the maximum margin condition or defaulting to a predetermined separation threshold if the calculated margin does not transgress the minimum margin condition or if the calculated margin transgresses the maximum margin condition.
  • the instructions to set the separation threshold configured to separate the R peaks from the other portions of the ECG signals further include instructions to count a number of the classified one or more true peaks. Determining the separation threshold is further based on whether the number of the classified one or more true peaks is greater than a predetermined true peak number.
  • the sequences of instructions further include instructions to perform an initial determination of an arrhythmia condition occurring in the patient based on an arrhythmia analysis of the ECG signals, identify and record, from the plurality of heart rates, an initial heart rate of the patient associated with the initial determination of the arrhythmia condition, and generate an initial heart rate score using the initial heart rate by identifying a first level heart rate range configured to be below a first level heart rate value, identifying a second level heart rate range configured to be above the first level heart rate value, and determining the initial heart rate score based on whether the initial heart rate of the patient is within the first level heart rate range or the second level heart rate range.
  • sequences of instructions further comprise instructions to determine, as part of the plurality of heart rates, a second heart rate subsequent to the initial heart rate of the patient for a period of time following the initial determination of the arrhythmia condition, generate an arrhythmia verification heart rate score using the initial heart rate score and the second heart rate, compare the arrhythmia verification heart rate score to a predetermined heart rate score threshold, and determine whether to initiate a treatment sequence for the patient based on the comparison of the arrhythmia verification heart rate score to the predetermined heart rate score threshold.
  • FIG. 2 depicts a timeline for using a heart rate score to verify whether a patient is experiencing an arrhythmia condition.
  • FIG. 4 depicts an example process flow for performing an arrhythmia analysis.
  • FIG. 5 depicts an example process flow for generating a heart rate score.
  • FIG. 6 depicts an example process flow for generating an arrhythmia verification heart rate score.
  • FIG. 7 depicts another sample process flow for using a heart rate score to verify an initial determination that a patient is experiencing an arrhythmia condition.
  • FIG. 8 depicts a sample process flow for using local maxima in ECG signals to determine a separation threshold used to identify R peaks.
  • FIG. 9 depicts an example of using a clustering process to classify local maxima in ECG signals.
  • FIG. 10 depicts another example of using a clustering process to classify 7 local maxima in ECG signals.
  • FIG. 11 depicts another example of using a clustering process to classify local maxima in ECG signals.
  • FIG. 12 depicts another process flow for using local maxima in ECG signals to determine a separation threshold used to identify R peaks.
  • FIG. 14 depicts an example process flow for sorting ECG intervals into histogram bins to determine a patient’s heart rate.
  • FIG. 15 depicts an example of calculating ECG intervals using R-peak detections.
  • FIG. 16 depicts an example histogram of ECG intervals.
  • FIG. 17 depicts another example histogram of ECG intervals.
  • FIG. 18 depicts another example histogram of ECG intervals.
  • FIG. 19 depicts an example process flow for determining ECG lead preference.
  • FIG. 20 depicts an example electronic architecture for a cardiac controller of a wearable cardiac defibrillator.
  • FIG. 21 depicts another example wearable cardiac defibrillator.
  • FIG. 22 depicts another example wearable cardiac defibrillator.
  • FIG. 23 depicts another example wearable cardiac defibrillator.
  • Wearable cardiac devices implementing the devices, systems, methods, and techniques disclosed herein can be used in clinical care settings to monitor for treatable cardiac arrhythmias and provide treatments such as defibrillation, cardioversion, or pacing shocks in the event of life-threatening arrhythmias.
  • the device is configured to detect and treat these life-threatening arrhythmias.
  • the wearable cardiac device may also provide alarms to the patient, warning the patient of an impending shock that the patient may be able to delay or cancel by pressing one or more response buttons thereby indicating that the patient is still conscious.
  • the wearable cardiac device detects that the patient is experiencing a cardiac arrhythmia condition and the patient is still conscious, the experience may be disruptive to the patient. For example, alarms warning the patient of an impending shock may be loud or startling, as the alarms are configured to gain the patient’s attention. Accordingly, decreasing the occurrence of false alarms without disrupting the wearable cardiac device’s ability to accurately sense cardiac arrhythmias may be beneficial for the patient experience. Not only does decreasing false alarms improve how the patient will likely perceive the desirability of using the wearable cardiac device once prescribed, but the patient’s view of the wearable cardiac device may impact the likelihood that the patient will comply with the prescription by wearing the wearable cardiac device continuously or nearly continuously.
  • this disclosure relates to wearable cardiac defibrillators that include sensing electrodes configured to sense electrical cardiac activity of the patient, therapy electrodes configured to deliver therapeutic shocks to the heart of the patient, and a cardiac controller operably connected to the sensing and therapy electrodes.
  • the wearable cardiac defibrillators further include a cardiac controller configured to use heart rate scores to determine, once a cardiac arrhythmia condition has been detected, whether to initiate a treatment sequence.
  • the heart rate scores represent a likelihood that the heart rate is associated with a cardiac arrhythmia as opposed to a false alarm.
  • the controller may be configured to make an initial determination that the patient is experiencing an arrhythmia condition and generate one or more heart rate scores for the heart rate associated with the initial determination and/or subsequent heart rates.
  • the wearable cardiac defibrillator may then compare the heart rate scores to a predetermined heart rate score threshold and determine whether to proceed to the treatment sequence based on the comparison.
  • the wearable cardiac defibrillators described herein may implement improvements to the process for generating the heart rate, as a more accurate heart rate may lead to more positive arrhythmia detections and fewer false alarms.
  • the wearable cardiac defibrillators may use a separation threshold to detect R peaks in the patient's electrocardiogram (ECG) signals generated from the patient’s sensed electrical cardiac activity.
  • ECG electrocardiogram
  • a given wearable cardiac defibrillator may periodically determine an updated separation threshold based on detecting local maxima in the patient’s ECG signals and identifying true peaks likely corresponding to R peaks in the ECG signals and false peaks likely corresponding to other peaks in the ECG signals.
  • the wearable cardiac defibrillator may evaluate the difference between the true peaks and false peaks to either generate an updated separation threshold or use a default separation threshold depending on how well an updated separation threshold would likely differentiate R peaks from other peaks in the ECG signals going forward. Thus, by using the local maxima to determine the updated (or default) separation threshold, the wearable cardiac defibrillator may more precisely detect R peaks in the ECG signals and thereby determine the patient’s heart rate.
  • the wearable cardiac defibrillators may use histograms to more accurately calculate the patient’s heart rate. More specifically, the wearable cardiac defibrillators may determine ECG intervals in the patient’s ECG signals, such as R-R intervals between detected R peaks, and sort the ECG intervals into histogram bins. Each histogram bin may be associated with a heart rate or a range or heart rates, so the wearable cardiac defibrillator may estimate the patient's heart rate based on the histogram bin having the greatest magnitude.
  • the histogram bins may also include a guard bin configured to catch ECG intervals that are more likely to be associated with noise, poor signal, signal artifacts, a heart rate beyond the resolution of the histogram bins, etc.
  • the guard bin is the histogram bin with the greatest magnitude
  • the wearable cardiac defibrillator may instead use a default method for estimating heart rate (e.g., calculating the heart rate based on the average R-R interval).
  • the wearable cardiac defibrillators may more correctly estimate the patient’s heart rate in the presence of noise or other signal artifacts, for example, which also leads to more accurate detections of cardiac arrhythmias.
  • the wearable cardiac defibrillators may include multiple ECG leads formed from pairs of ECG sensing electrodes.
  • the wearable cardiac defibrillators may thus periodically or continuously update which of the ECG sensing leads the wearable cardiac defibrillator uses as the default sensing lead for determining the patient’s heart rate.
  • a given wearable cardiac defibrillator may determine at least one test heart rate for each sensing lead using the ECG signals generated via each respective sensing lead and determine whether the test heart rates from the various sensing leads agree with each other. If the test heart rates agree, the wearable cardiac defibrillator maintains using the ECG signals from the currently- set preferred sensing lead, for example, to generate the patient’s estimated heart rate.
  • the wearable cardiac defibrillator determines whether one or more conditions are satisfied for switching the preferred lead to one of the other sensing leads. In this way, the wearable cardiac defibrillator uses the best quality ECG signals to generate the patient’s heart rate, which in turn may lead to more accurate identifications of whether the patient is experiencing a cardiac arrhythmia.
  • a cardiologist may prescribe that a patient at risk for developing a life-threatening arrhythmia use a wearable cardiac defibrillator until the patient can receive an implantable defibrillator.
  • the wearable cardiac defibrillator may apply one or more of the techniques described above for improving the accuracy of determining that the patient is experiencing a cardiac arrhythmia condition. For example, the wearable cardiac defibrillator may use heart rate scores to evaluate whether the heart rate is likely associated with a cardiac arrhythmia given an initial determination that the patient is experiencing a cardiac arrhythmia condition. As another example, the wearable cardiac defibrillator may use an analysis of local maxima in the patient's ECG signals to periodically determine an updated separation threshold used to detect R waves in the patient's ECG signals, where the R-wave detections are then used to estimate the patient’s heart rate.
  • the wearable cardiac defibrillator may determine the patient’s heart rate by calculating ECG intervals for the patient’s ECG signals, sorting the ECG intervals into histogram bins, and using the histogram bin with the greatest magnitude to generate the patient’s heart rate.
  • the wearable cardiac defibrillator may continuously or periodically determine the quality of the heart rate being generated from a preferred ECG sensing lead compared to another or other ECG sensing leads. If the wearable cardiac defibrillator determines that certain conditions are present in the heart rate and/ or signal quality 7 from the preferred EC G sensing lead, the wearable cardiac defibrillator may switch the preferred sensing lead to another sensing lead, thereby helping to ensure more accurate heart rate detection.
  • the wearable cardiac defibrillator may implement improved cardiac arrhythmia condition detection.
  • the wearable cardiac defibrillators described herein may provide advantages over prior art systems. As discussed above, experiencing a false detection that the patient is having a cardiac arrhythmia may be unpleasant to the patient given that the alarms warning the patient that a defibrillation or other shock is imminent may be loud and disruptive to the patient. Additionally, if a patient does not indicate to the wearable cardiac defibrillator that the patient is still conscious after receiving a false detection, the wearable cardiac defibrillator may provide the patient with an unnecessary treatment.
  • the techniques described herein relating to lead preference, generating heart rate, and heart rate scores may improve the accuracy of determining that the patient is experiencing a cardiac arrhythmia condition, as discussed above.
  • these techniques may decrease the amount of false alerts (and potentially unnecessary treatments) that patients receive, therefore improving the patient experience with wearing and using the wearable cardiac defibrillators.
  • patient compliance with a prescription to use a wearable cardiac defibrillator may similarly be improved.
  • a wearable cardiac defibrillator may record an ECG segment when the wearable cardiac defibrillator determines that the patient is experiencing a cardiac arrhythmia.
  • the wearable cardiac defibrillator may transmit the recorded ECG segment to a remote server for a patient’s caregiver to later review.
  • the wearable cardiac defibrillator makes a false detection that the patient is experiencing a cardiac arrhythmia, this results in an unneeded recording for the patient’s caregiver. Unneeded recordings can make it more difficult for the patient’s caregiver to review and identify cardiac issues in the caregiver’s patients, especially when the caregiver has a large number of patients using a wearable cardiac defibrillator.
  • decreasing false detections may also improve the caregiver’s experience with the wearable cardiac defibrillator and increase the caregiver’s confidence in prescribing the wearable cardiac defibrillator to patients who could benefit from its monitoring and treatment capabilities.
  • FIG. 1 illustrates an example of a wearable cardiac defibrillator 200, according to implementations disclosed herein.
  • the wearable cardiac defibrillator 200 is external and wearable by a patient 202 around the patient’s torso.
  • Such a wearable cardiac defibrillator 200 can be, for example, capable and designed for moving with the patient 202 as the patient 202 goes about their daily routine.
  • the wearable cardiac defibrillator 200 may be configured to be bodily-attached to the patient 202, such as by the patient 202 donning the wearable cardiac defibrillator 200 and securing the wearable cardiac defibrillator 200 in place through a closure.
  • the wearable cardiac defibrillator 200 can be worn nearly continuously or substantially continuously for an extended period of time, such as a week, two weeks, a month, two months, three months, six months, etc.
  • the wearable cardiac defibrillator can be configured to continuously or substantially continuously monitor the vital signs of the patient 202 and can be configured to, upon determination that treatment is required, deliver one or more therapeutic electrical pulses to the patient 202.
  • therapeutic shocks can be pacing, defibrillation, cardioversion, and/or transcutaneous electrical nerve stimulation (TENS) pulses.
  • the wearable cardiac defibrillator 200 may be a wearable cardioverter defibrillator and configured to synchronize therapeutic shocks with the patient's cardiac cycles.
  • the wearable cardiac defibrillator 200 can include one or more ECG sensing electrodes 204a-d (collectively referred to herein as sensing electrodes 204) configured to contact the patient’s skin and sense electrical cardiac activity of the patient 202, one or more therapy electrodes 206a and 206b (collectively referred to herein as therapy electrodes 206) configured to deliver therapeutic shocks to the heart of the patient 202, and a cardiac controller 214 operably connected to the sensing electrodes 204 and the therapy electrodes 206.
  • ECG sensing electrodes 204a-d collectively referred to herein as sensing electrodes 204
  • therapy electrodes 206a and 206b collectively referred to herein as therapy electrodes 206
  • a cardiac controller 214 operably connected to the sensing electrodes 204 and the therapy electrodes 206.
  • the wearable cardiac defibrillator 200 can also include a garment 208 configured to worn about the torso of the patient 202, where the garment 208 is configured to support the sensing electrodes 204 and the therapy electrodes 206.
  • the garment 208 may be configured in a vest-like configuration for wear over the patient’s upper torso.
  • the wearable cardiac defibrillator 200 can include additional elements, such as a connection pod 210, a patient interface pod 212, additional sensors or detectors, or any combination of these.
  • additional sensors or detectors include one or more motion detectors configured to generate motion data indicative of physical activity being performed by the patient 202, wear state sensors configured to detect a wear state of the wearable cardiac defibrillator 200 (e.g., whether the patient 202 is wearing the wearable cardiac defibrillator 200 or not), vibrational or bioacoustics sensors configured to generate bioacoustics signals for the heart of the patient 202, respiration sensors configured to generate respiration signals indicative of respiration activity of the patient 202, thoracic fluid sensors configured to generate thoracic fluid signals indicative of a thoracic fluid level of the patient 202, and/or the like.
  • wear state sensors configured to detect a wear state of the wearable cardiac defibrillator 200 (e.g., whether the patient 202 is wearing the wearable cardiac defibrillator 200 or not)
  • vibrational or bioacoustics sensors configured to generate bioacoustics signals for the heart of the patient 202
  • respiration sensors configured to generate respiration signals indicative of
  • some or all of the sensing electrodes 204, the therapy electrodes 206, the connection pod 210, the patient interface pod 212, additional sensors or detectors, and the like may be configured to be assembled onto the garment 208.
  • at least some of the components of the wearable cardiac defibrillator 200 can be configured to be removably mounted or affixed on the garment 208, such as by mating hooks, hook-and-loop fabric strips, receptacles (e.g., pockets), snaps (e.g., plastic or metal snaps), and the like.
  • the sensing electrodes 204 may be removably attached to the garment 208 by hook-and-loop fabric strips on the sensing electrodes 204 and the garment 208.
  • the therapy electrodes 206 may be removably attached to the garment 208 by being inserted into receptacles on the garment 208.
  • at least some of the components of the wearable cardiac defibrillator 200 can be permanently integrated into the garment 208, such as by being sewn into the garment 208 or by being adhesively secured to the garment 208 with a permanent adhesive.
  • the components may be connected to each other through external cables, through internal or sewn-in connections (e.g., wires woven into the fabric of the garment 208), through conductive fabric of the garment 208, and/or the like.
  • Component configurations other than those shown in FIG. 1 are also possible.
  • the sensing electrodes 204 may be configured to be attached at other positions about the body of the patient 202 and may include additional or fewer electrodes 204.
  • the cardiac controller 214 can be operatively coupled to the sensing electrodes 204 and the therapy electrodes 206.
  • the cardiac controller 214 may be directly coupled to at least some of the sensing electrodes 204 and/or therapy electrodes 206.
  • the cardiac controller 214 may be indirectly coupled to at least some of the sensing electrodes 204 and/or therapy electrodes 206, such as through another component of the wearable cardiac defibrillator 200 like the connection pod 210.
  • the cardiac controller 214 may also be configured to be assembled into the garment 208. For example, the entire cardiac controller 214 as shown in FIG.
  • the functions of the cardiac controller 214 may be dispersed among multiple cardiac controller units (e.g., a cardiac arrhythmia monitoring unit, a therapy delivery unit, a communications unit, an alarm unit, etc.).
  • the multiple cardiac controller units may include individual units for each of some or all of the components shown in the electronic architecture of FIG. 20. These multiple cardiac controller units may then be inserted into and/or attached to receptacles of the garment 208.
  • the sensing electrodes 204 can be configured to sense electrical cardiac activity of the patient 202.
  • Example sensing electrodes 204 may include a metal electrode with an oxide coating such as tantalum pentoxide electrodes.
  • the sensing electrodes 204 can include skin-contacting electrode surfaces that may be deemed polarizable or non-polarizable depending on a variety of factors including the metals and/or coatings used in constructing the electrode surface. All such electrodes can be used with the principles, techniques, devices, and systems described herein.
  • the electrode surfaces can be based on stainless steel, noble metals such as platinum, or Ag-AgCl.
  • the sensing electrodes 204 can be used with an electrolytic gel dispersed between the electrode surface and the patient's skin.
  • the sensing electrodes 204 can be dry electrodes that do not need an electrolytic material.
  • such a dry electrode can be based on tantalum metal, such as by having a tantalum pentoxide coating as is described above. Such dry electrodes can be more comfortable for long-term monitoring applications.
  • the sensing electrodes 204 can include additional components such as accelerometers, acoustic signal detecting devices (e.g., vibrational sensors), and other measuring devices for recording other types of parameters for the patient 202.
  • the sensing electrodes 204 can also be configured to detect other patient physiological parameters and acoustic signals, such as tissue fluid levels, heart vibrations, lung vibrations, respiration vibrations, patient movement, etc.
  • the therapy electrodes 206 can additionally or alternatively be configured to include sensors configured to detect electrical cardiac activity of the patient 202 as well as, or in the alternative to. other physiological parameters or signals from the patient 202.
  • connection pod 210 can, in various examples, include a signal processor configured to amplify, filter, and digitize signals (e.g., ECG signals generated from the sensed electrical cardiac activity of the patient 202) prior to transmitting the signals to the cardiac controller 214.
  • the connection pod 210 may be configured to reduce and/or remove noise in the signals received from the sensing electrodes 204 (e.g., based on ground signals from a ground electrode, which may be one of the sensing electrodes 204, one of the therapy electrodes 206, and/or provided elsewhere on the wearable cardiac defibrillator 200).
  • connection pod 210 may be configured to digitize the signals received from the sensing electrodes 204, such as through an analog-to-digital converter.
  • the sensing electrodes 204 themselves may include circuitry to digitize the ECG signals, and the connection pod 210 may receive the digitized ECG signals from the sensing electrodes 204.
  • the connection pod 210 may then, for example, perform other processing of the digitized ECG signals.
  • the connection pod 210 may filter the digitized ECG signals to remove noise.
  • the connection pod 210 may generate the ground signals used to determine whether a given sensing electrode 204 has fallen off the patient’s body (e.g., with the determination performed at the connection pod 210 or at the cardiac controller 214).
  • connection pod 210 is shown as attached to the garment 208 at patient’ s front in FIG. 1, in examples the connection pod 210 may be attached to the garment 208 at the small of the patient’s back. In such implementations, because the connection pod 210 at located against the small of the patient’s back where the patient may be sensitive to feeling movement, the connection pod 210 can be configured to include one or more vibration motors to provide tactile notifications to the patient 202. For instance, the connection pod 210 can receive one or more signals from the cardiac controller 214 and provide a tactile alert to the patient 202 based on the one or more signals from the cardiac controller 214, as described in further detail below.
  • the cardiac controller 214 is configured to monitor the ECG signals generated from the electrical cardiac activity sensed by the sensing electrodes 204 and determine when the patient 202 is experiencing a treatable cardiac arrhythmia.
  • the therapy electrodes 206 are configured to deliver one or more electrical therapeutic shocks to the patient 202, such as one or more therapeutic cardioversion/defibrillation shocks to the body of the patient 202, when the cardiac controller 214 determines that treatment is warranted.
  • Example therapy electrodes 206 can include conductive metal electrodes such as stainless-steel electrodes.
  • the therapy electrodes 206 include one or more conductive gel deployment devices configured to deliver conductive gel between the metal electrode and the patient’s skin prior to delivery of a therapeutic shock.
  • the cardiac controller 214 is configured to determine whether the patient 202 is experiencing a treatable cardiac arrhythmia based on the patient’s ECG signals.
  • the cardiac controller 214 is further configured to instruct delivery of one or more electrical therapeutic shocks to the patient 202 via the therapy electrodes 206 in response to determining that the patient 202 is experiencing a treatable cardiac arrhythmia.
  • the functionality of the cardiac controller 214 is described in further detail below with respect to FIG. 20.
  • connection pod 210 may be configured to control at least part of the delivery of the therapeutic shocks.
  • the connection pod 210 may receive a signal from the cardiac controller 214 initiating a therapy delivery' sequence.
  • the connection pod 210 may send a signal to the therapy electrodes 206 to activate the deployment of conductive electrolytic gel at the therapy electrodes 206 (e.g.. from an integrated or removable gel pack on each therapy electrode 206).
  • the connection pod 210 may then receive one or more therapeutic charges from the cardiac controller 214 and convey the one or more therapeutic charges to the therapy electrodes 206 for delivery of the one or more therapeutic shocks to the patient 202.
  • the cardiac controller 214 is also configured to warn the patient 202 prior to the delivery of a therapeutic shock, such as via one or more output device integrated into or connected to the cardiac controller 214, the connection pod 210, and/or the patient interface pod 212.
  • the patient interface pod 212 can be secured to a hook- and-loop fastener, or a plastic or metal snap connector, a clip, a buckle, etc. disposed on the shoulder strap of the garment 208 and/or on the patient interface pod 212.
  • the warning may be auditory (e.g., a siren alarm, a voice instruction indicating that the patient 202 is going to be shocked, etc.), visual (e.g., flashing lights on the cardiac controller 214, etc.), haptic (e.g., a tactile, buzzing alarm generated by the connection pod 210. etc.), and/or the like.
  • the cardiac controller 214 may deliver alarms warning the patient 202 of an impending therapeutic shock via speakers on the cardiac controller 214 or the connection pod 210 (e.g...
  • the cardiac controller 214 may deliver alarms via a screen or other lights of the cardiac controller 214 or patient interface pod 212 (e.g.. by displaying a warning on the screen of the cardiac controller 214. by lighting up LEDs on the cardiac controller 214, and/or the like).
  • the cardiac controller 214 may deliver alarms by causing the connection pod to vibrate against the patient’s back, as described above.
  • alarms may include combinations of some or all of these examples.
  • alarms may escalate over time, such as by including louder sounds, higher frequencies, more frequent warnings, stronger vibrations, and/or the like.
  • the patient interface pod 212 may include one or more response buttons that the patient 202 must push to delay or cancel the therapeutic shock.
  • the patient 202 may need to press a response button on the patient interface pod 212 and the cardiac controller 214 simultaneously to delay or cancel the therapeutic shock.
  • the wearable cardiac defibrillator 200 may include a separate response button unit that contains the one or more response buttons.
  • the wearable cardiac defibrillator 200 may include a wireless response button unit, for example, implemented as a watch or wristband that the patient 202 wears along with the garment 208.
  • a patient user device such as a smartphone, may serve as the response button unit.
  • the alarms warning the patient of the impending treatment shock can be loud and occur at inopportune times.
  • described herein are implementations for increasing the accuracy of how the wearable cardiac defibrillators described herein, such as the wearable cardiac defibrillator 200, identifies that the patient using the wearable cardiac defibrillator is experiencing a treatable cardiac arrhythmia, thereby decreasing the occurrence of false alarms.
  • FIG. 2 illustrates an example timeline 300 for using a heart rate (HR) score to verify whether a patient using a wearable cardiac defibrillator is experiencing a treatable cardiac arrhythmia.
  • the wearable cardiac defibrillator discussed with respect to FIG. 2 is the wearable cardiac defibrillator 200, though the systems, methods, and apparatuses discussed herein can be used with any of the implementations of a wearable cardiac defibrillator herein described. Additional details of the processes described with respect to the example timeline 300 are also discussed in further detail with respect to the sample process flow of FIG. 3 below.
  • the wearable cardiac defibrillator 200 continuously monitors the patient’s ECG signals (e.g., generated from the electrical cardiac activity' sensed by the sensing electrodes 204) to evaluate whether the patient 202 is suspected of experiencing a treatable cardiac arrhythmia.
  • ECG signals e.g., generated from the electrical cardiac activity' sensed by the sensing electrodes 20
  • the wearable cardiac defibrillator 200 performs an initial determination that the patient 202 is experiencing an arrhythmia condition based on an arrhythmia analysis of the ECG signals.
  • the wearable cardiac defibrillator 200 identifies an initial heart rate of the patient 202 that is associated with the initial determination of the arrhythmia condition. For example, the wearable cardiac defibrillator 200 identifies the heart rate of the patient 202 at point 302.
  • the wearable cardiac defibrillator 200 also generates an initial HR score for the initial heart rate of the patient 202, where the initial HR score is associated with how likely it is that the patient 202 is actually experiencing the arrhythmia condition (e.g., with a higher score indicating that it is more likely the patient 202 is experiencing the arrhythmia condition and a lower score indicating that it is less likely that the patient 202 is experiencing the arrhythmia condition).
  • the wearable cardiac defibrillator 200 then establishes a countdown period 304 that the wearable cardiac defibrillator 200 waits before the wearable cardiac defibrillator 200 will definitively declare that the patient 202 is experiencing the arrhythmia condition.
  • the countdown period 304 may be a period of time during which the wearable cardiac defibrillator 200 uses a second arrhythmia analysis to confirm whether the patient 202 is experiencing the arrhythmia condition.
  • the countdown period 304 may be a period of time during which the wearable cardiac defibrillator 200 gathers more data to use in confirming whether the patient 202 is experiencing the arrhythmia condition (e.g., determining one or more additional heart rates to use in generating an HR score, as discussed in further detail below).
  • the wearable cardiac defibrillator 200 generates an arrhythmia verification HR score for the patient 202. Similar to the initial HR score, the arrhythmia verification HR score is associated with how likely it is that the patient 202 is actually experiencing the arrhythmia condition, for example, based on the patient's current heart rate and/or recent past heart rates. In implementations, the arrhythmia verification HR score is based on the patient’s current heart rate data and the initial HR score.
  • the wearable cardiac defibrillator 200 implements up to two verification periods. After point 306, the w earable cardiac defibrillator 200 waits for a first verification period 308.
  • the first verification period 308 may be the same or a similar length to the countdown period 304, or the first verification period 308 may be a different length from the countdown period 304.
  • the wearable cardiac defibrillator 200 calculates a second arrhythmia verification HR score.
  • the wearable cardiac defibrillator 200 declares the arrhythmia condition at point 310. If the second arrhythmia verification HR score does not transgress the predetermined HR score threshold, the wearable cardiac defibrillator 200 implements a second verification period 312. At point 314. at the end of the second verification period 312, the wearable cardiac defibrillator 200 generates a third arrhythmia verification HR score. If the third arrhythmia verification HR score transgresses the predetermined HR score threshold, the wearable cardiac defibrillator 200 declares the arrhythmia condition at point 314.
  • the wearable cardiac defibrillator 200 decreases the confidence level in the initial determination of the arrhythmia condition. For example, in implementations, the wearable cardiac defibrillator 200 may drop the determination of the arrhythmia condition entirely if the third arrhythmia verification HR score does not transgress the predetermined HR score threshold. In implementations, if the third arrhythmia verification HR score does not transgress the predetermined HR score threshold, the wearable cardiac defibrillator 200 may only initiate a therapy sequence if a second arrhythmia analysis also indicates that the patient 202 is experiencing the arrhythmia condition.
  • the wearable cardiac defibrillator 200 may implement additional or fewer verification countdown periods.
  • the wearable cardiac defibrillator 200 may implement verification countdown periods for a certain period of time, such as a minute, until the arrhythmia verification HR score either transgresses the predetermined HR score threshold or the minute passes.
  • the wearable cardiac defibrillator 200 may take additional actions at each of the points after a verification countdown period. For example, the wearable cardiac defibrillator 200 may determine a confidence level in the initial determination of the cardiac arrhythmia.
  • the wearable cardiac defibrillator 200 may decrease a confidence level of the initial determination of the arrhythmia condition every time the arrhythmia verification HR score does not transgress the predetermined HR score threshold. If the confidence level drops low enough, such as below a predetermined confidence level threshold, the wearable cardiac defibrillator 200 may drop the determination of the arrhythmia condition.
  • FIG. 3 illustrates a sample process flow for using the HR score to verify an initial determination of an arrhythmia condition, as described in high level with respect to the example timeline 300 of FIG. 2.
  • the sample process 400 shown in FIG. 3 can be implemented by one or more processors of a wearable cardiac defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200.
  • Other embodiments of wearable cardiac defibrillators, including the other embodiments described herein, may similarly implement the sample process 400.
  • the processor is configured to perform an initial determination of an arrhythmia condition occurring in the patient 202 at step 402.
  • the processor is configured to perform the initial determination of the arrhythmia condition through an arrhythmia analysis of ECG signals generated from the electrical cardiac activity sensed by the sensing electrodes 204.
  • the arrhythmia analysis includes performing a heart rate analysis of the patient’s heart rate.
  • the arrhythmia analysis performing a heart rate analysis of the patient’s heart rate and performing a morphological analysis of the patient’s ECG signals and/or a frequency analysis of the patient’s ECG signals.
  • FIG. 4 illustrates a sample process flow for the arrhythmia analysis.
  • the sample process flow 500 can be implemented by a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200, or by one or more processors of another embodiment of a wearable cardiac defibrillator.
  • the processor determines the patient’s heart rate from the ECG signals at step 502.
  • the processor may use a QRS detector on the ECG signals to determine the patient’s heart rate.
  • the processor may determine the patient’s heart rate by performing a fast Fourier transform (FFT) on the ECG signals, with the FFT decomposing the analog ECG waveform into its frequency components.
  • the processor may then analyze the output of the FFT to determine the strongest frequency component indicative of heart rate.
  • the heart rate determined at step 502 may be the initial heart rate.
  • the processor determines if the patient’s heart rate transgresses (e.g., if the patient’s heart rate is greater than, if the patient’s heart rate is greater than or equal to, etc.) an arrhythmia threshold at step 504.
  • the processor may determine if the patient’s heart rate transgresses a general arrhythmia threshold (e.g., 150 bpm, 160 bpm. 170 bpm, etc.).
  • the processor may determine if the patient’s heart rate transgresses an arrhythmia threshold used for a specific arrhythmia.
  • the processor may determine if the patient’s heart rate is below a threshold for ventricular tachycardia, at or above the threshold for ventricular tachycardia but below a threshold for ventricular fibrillation, or at or above the threshold for ventricular fibrillation. As another illustration, the processor may determine if the patient’s heart rate is at or below athreshold for bradycardia, at or above athreshold for atrial fibrillation, at or above a threshold for ventricular tachycardia, and/or at or above a threshold for ventricular fibrillation. In implementations, these arrhythmia thresholds may be preset (e.g., based on defaults for the wearable cardiac defibrillator 200).
  • these thresholds may be user-configurable or programmable for the patient 202. For instance, these thresholds may be programmed by a physician or a technician during a setup period at which the patient 202 is fitted for and receives the wearable cardiac defibrillator 200.
  • the processor then performs a morphological analysis of the patient’s ECG signals and/or a frequency analysis of the patient’s ECG signals.
  • the processor determines the patient’s current vectorcardiogram from the ECG signals at step 506.
  • the ECG sensors 204 may be positioned around the patient’s torso when the patient is wearing the wearable cardiac defibrillator 200 to form orthogonal leads (e.g., front-to-back and side-to-side at the level of the patient’s xiphoid process).
  • the processor may determine a direction and magnitude of the electrical forces in the patient’s heart and plot them (e.g., on an x-y or an x-y-z graph) to form a vectorcardiogram.
  • the processor may determine the patient’s vectorcardiogram in response to identifying that the patient’s heart rate transgresses an arrhythmia threshold at step 504.
  • the processor may determine the patient’s current vectorcardiogram independent of whether the patient's heart rate transgresses an arrhythmia threshold at step 504.
  • the processor may determine the patient’s current vectorcardiogram as part of determining the patient’s heart rate from the ECG signals.
  • the processor may plot the patient’s current vectorcardiogram, determine the amount of time that it takes for the vectorcardiogram to repeat (e.g., for the plot to return to a new starting point of within a certain vicinity of an original starting point), and use that determination to output a heart rate for the patient 202.
  • the processor determines whether the patient’s current vectorcardiogram matches a baseline vectorcardiogram at step 508.
  • the processor may take a baseline vectorcardiogram for the patient 202 during a setup period and/or periodically during the patient’s use of the wearable cardiac defibrillator 200 (e.g., weekly at a predetermined time, after the patient 202 is delivered a therapeutic shock, etc.).
  • the processor may then compare the patient’s current vectorcardiogram to the patient’s baseline vectorcardiogram to determine if the two morphologies match with a predetermined degree of accuracy.
  • the predetermined degree of accuracy may vary for the different types of arrhythmias that the processor can detect.
  • the processor may determine whether the patient’s cunent vectorcardiogram matches their baseline vectorcardiogram only if the processor has already determined that the patient’s heart rate transgresses an arrhythmia threshold at step 504. In implementations, processor may not perform step 508 under certain conditions.
  • the processor may not perform step 508 if the signal quality from one of the ECG sensors 204 is unreliable or if the patient’s heart rate is above the ventricular fibrillation threshold. In such cases, the processor may instead rely primarily on other measures, such as the heart rate analysis, the patient’s heart rate stability (e.g.. whether the R-R intervals of the patient’s heart rate are consistent or inconsistent), onset criteria (e.g., whether the patient has experienced rapid changes in heart rate), and/or the like.
  • other measures such as the heart rate analysis, the patient’s heart rate stability (e.g.. whether the R-R intervals of the patient’s heart rate are consistent or inconsistent), onset criteria (e.g., whether the patient has experienced rapid changes in heart rate), and/or the like.
  • the processor outputs an indication as to whether the patient 202 is experiencing the arrhythmia at step 510. For example, if the patient’s heart rate does not transgress an arrhythmia threshold, the processor may output an indication that the patient 202 is not experiencing an arrhythmia condition. As another example, if the patient’s heart rate transgresses an arrhythmia threshold but the patient’s current vectorcardiogram matches their baseline vectorcardiogram with the predetermined degree of accuracy, the processor may also output an indication that the patient 202 is not experiencing an arrhythmia condition.
  • the processor may output an initial determination that the patient 202 is experiencing an arrhythmia condition.
  • a first arrhythmia threshold e.g., a threshold for ventricular fibrillation
  • a second arrhythmia threshold e.g., a threshold for ventricular tachycardia that is higher than the threshold for ventricular fibrillation
  • the processor may output an initial determination that the patient 202 is experiencing a treatable arrhythmia.
  • the processor may output an indication that the patient 202 is experiencing a non- treatable arrhythmia condition regardless of whether the patient’s current vectorcardiogram fails to match their baseline vectorcardiogram.
  • a non-treatable cardiac arrhythmia e.g., a threshold for ventricular tachycardia
  • the processor applies a confidence level as part of determining whether the patient 202 is experiencing an arrhythmia condition.
  • the processor may assign weights to various inputs to determine a confidence level for whether the patient 202 is experiencing an arrhythmia condition. These input may include, for example, the patient’s heart rate, vectorcardiogram morphology, response button use (e.g., whether the patient 202 has already been alerted to the arrhythmia condition and pressed the one or more response buttons of the wearable cardiac defibrillator 200). signal quality, and/or the like.
  • the weighted inputs can contribute positively or negatively to the confidence level.
  • the processor may decrease the weight for that input.
  • the processor outputs an initial determination that the patient 202 is experiencing an arrhythmia condition if the confidence level transgresses a predetermined confidence level threshold and otherwise outputs an indication that the patient is not experiencing an arrhythmia condition.
  • the processor identifies and records an initial heart rate of the patient 202 associated with the initial determination of the arrhythmia condition.
  • the patient’s initial heart rate may be the heart rate that the processor used in making the initial determination of the arrhythmia condition.
  • the processor may identify the heart rate determined at step 502 and used in step 504 of FIG. 4 as the patient’s initial heart rate.
  • the patient’s initial heart rate may be the patient’s current heart rate at point 302 of timeline 300 of FIG. 2, as determined by the wearable cardiac defibrillator 200.
  • the current heart rate may be the same as a heart rate used in making the initial determination of the arrhythmia condition or it may be a more current heart rate.
  • the processor generates an initial HR score for the patient 202 at step 406.
  • the initial HR score is specifically for the initial heart rate identified at step 404.
  • FIG. 5 illustrates a sample process flow for generating an HR score.
  • the sample process flow 600 can be implemented by a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200, or by one or more processors of another embodiment of a w earable cardiac defibrillator.
  • the processor identifies a first level heart rate (HR) range at step 602 and identifies a second level HR range at step 604.
  • HR heart rate
  • the first level HR value may be a HR value associated with detecting an arrhythmia condition and/or detecting a particular type of arrhythmia condition.
  • the first level HR value may be a heart rate threshold that the wearable cardiac defibrillator 200 uses to detect ventricular tachycardia or ventricular fibrillation (e.g., as part of implementing step 504 of FIG. 4).
  • the first level HR value may be a value in a predetermined range common for treatable cardiac arrhythmias, such as a value in between 130 bpm and 180 bpm inclusive. [0106]
  • the first level HR value may be a predetermined HR value.
  • the first level HR value may be a preset value.
  • the first level HR value may be between 60- 120 bpm, 80-120 bpm. 100-120 bpm, 130-180 bpm, etc., as discussed above.
  • the first level HR value may be a user-configurable or programmable HR value.
  • the first level HR value may be programmable by the patient's physician and/or by a technician for the wearable cardiac defibrillator 200.
  • the first level HR value may correspond to a heart rate threshold that the wearable cardiac defibrillator 200 uses to detect one or more types of arrhythmia conditions. This heart rate threshold may itself be user-configurable.
  • the predetermined HR value may be user-configurable or programmable, such as to deviate from a default value programmed into the wearable cardiac defibrillator 200.
  • a physician or technician may be able to select the predetermined HR value by typing in a heart rate value into a user interface of the cardiac controller 214 or selecting an HR value from a range provided by the cardiac controller 214 (e.g., 60-120 bpm, 80-120 bpm, 100-120 bpm, 130-180 bpm, etc.).
  • the processor may use more than two heart rate ranges in generating the initial HR score. For example, as shown in FIG. 5, the processor may optionally identify a third level HR range at step 606.
  • the third level HR range may be above a second level HR value, where the second level HR value is greater than the first level HR value.
  • the second level HR range may accordingly lie between the first level HR value and the second level HR value.
  • the processor may set the second level HR value similarly to the processes used to set the first level HR value described above.
  • the second level HR value may be a predetermined HR value.
  • the second level heart rate value may be a user-configurable or programmable HR value.
  • the processor may use additional heart rate ranges in generating the initial HR score. For example, the processor may also use a fourth HR range configured to be above a third level HR value that is greater than the second level HR value, where the third level HR range lies between the second level HR value and the third level HR value. As another example, the processor may use a fifth level HR range configured to be above a fourth level HR value that is greater than the third level HR value, where the fourth level HR range lies between the third level HR value and the fourth level HR value, and so on. In embodiments, the processor may additionally or alternatively use heart rate ranges that are below and/or between the heart rate ranges described above.
  • the amount and/or type of heart rate ranges that the processor uses may be user-configurable or programmable. For instance, the patient’s physician or a technician for the wearable cardiac defibrillator 200 may set the number of heart rate ranges and/or what metrics determine the associated heart rate values that define the heart rate ranges (e.g., whether each heart rate value is preset or associated with a particular arrhythmia threshold).
  • the processor may use three heart rate ranges in generating the initial HR score.
  • the first level HR range may lie below a first level HR value that is associated with an elevated heart rate, such as a first level HR value between 60 bpm and 120 bpm inclusive.
  • the second level HR range may lie between the first level HR value and a second level HR value, where the second level HR value is associated with detecting a particular type of arrhythmia.
  • the second level HR value may be a user- configurable threshold used to detect ventricular tachycardia.
  • the third level HR range may he above the second level HR value.
  • the processor determines the initial heart rate score based on which range the initial heart rate falls under at step 608. For example, if the processor is using two heart ranges, the processor may identify whether the initial heart rate is within a first level HR range or a second level HR range. If the processor is using three heart rate ranges, the processor may identify whether the initial heart rate is within a first level HR range, a second level HR range, or a third level HR range, and the like. The processor then determines the heart rate score based on the identified heart rate range for the initial heart rate.
  • the processor may assign the initial heart rate a score amount based on the identified heart rate range. As an illustration, if the processor is using two heart rate ranges, the processor may assign the initial heart rate a first heart rate (HR) score amount on determining that the initial heart rate is within the first level HR range. However, if the initial heart rate is instead within the second level HR range, the processor may assign the initial heart rate a second HR score amount, where the second HR score amount is greater than the first HR score amount. For example, the processor may assign the initial heart rate a 0 if the initial heart rate is within the first level HR range or assign the initial heart rate a 1 if the initial heart rate is within the second level HR range.
  • HR heart rate
  • the processor may assign the initial heart rate a first HR score amount or a second HR score amount as described above.
  • the processor may alternatively assign the initial heart rate a third HR score amount on determining that the initial heart rate range is within the third level HR range as opposed to the first or second level HR ranges. For example, the processor may assign the initial heart rate a 0 if the initial heart rate is within the first level HR range, a 10 if the initial heart rate is within the second level HR range, or a 15 if the initial heart rate is within the third level HR range. Additional examples of assigning heart rate score amounts to generate the initial HR score are described below with reference to Tables 1-4.
  • the processor determines one or more additional heart rates subsequent to the initial heart rate at step 408.
  • the one or more additional heart rates may include, for example, at least a second heart rate.
  • the processor determines the one or more additional heart rates for a period of time following the initial determination of the arrhythmia condition at step 402. For instance, referring back to the example timeline 300, the processor may implement the countdown period 304, during which the processor continues to determine and record the patient's current heart rate.
  • the processor is configured to generate an arrhythmia verification heart rate (HR) score at step 410.
  • the processor may first compare the initial HR score to a predetermined heart rate (HR) score threshold, described below in more detail with respect to step 412. If the initial HR score does not transgress the predetermined HR score threshold, the processor may proceed to step 410 and generate the arrhythmia verification HR score. Otherwise, if the initial HR score does transgress the predetermined HR score threshold, the processor may declare that the patient 202 is experiencing the arrhythmia condition and proceed to initiating a treatment sequence. In implementations, the processor may be configured to proceed to step 410 without comparing the initial HR score to the predetermined HR score threshold. As such, the processor may generate the arrhythmia venfication HR score regardless of whether the initial HR score transgresses the predetermined HR score threshold or not.
  • HR heart rate
  • the processor is configured to generate the arrhythmia verification HR score using the initial HR score and/or the one or more additional heart rates subsequent to the initial heart rate (e.g., at least a second heart rate) determined at step 408.
  • FIG. 6 illustrates a sample process flow for generating the arrhythmia verification HR score.
  • the sample process flow 700 can be implemented by a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200, or by one or more processors of another embodiment of a wearable cardiac defibrillator.
  • the processor calculates a median heart rate from past heart rates at step 702. In implementations, these past heart rates include a predetermined number of consecutive past heart rates.
  • the predetermined number of consecutive past heart rates used to calculate the median heart rate may range, for instance, from 2 to 200 inclusive. As an example, the predetermined number of consecutive past heart rates used may be between 5 and 20 inclusive. In implementations, the predetermined number of the consecutive past heart rates used may be preset. In implementations, the predetermined number of the consecutive past heart rates used may be user-configurable or programmable, such as by a physician for the patient 202 or by a technician for the wearable cardiac defibrillator 200.
  • the processor can continuously determine and record heart rates for the patient 202.
  • the processor may calculate the median heart rate based on a certain number of the heart rates most recently recorded for the patient 202.
  • These heart rates most recently recorded for the patient 202 may include the one or more additional heart rates (e.g., at least the second heart rate) determined at step 408 of FIG. 3.
  • these heart rates most recently recorded for the patient 202 may only include the additional heart rate(s) determined during a countdown period, such as the countdown period 304 of example timeline 300.
  • these heart rates most recently recorded for the patient 202 may further include the initial heart rate.
  • these heart rates most recently recorded for the patient 202 may further include one or more heart rates determined and recorded before the initial heart rate.
  • the processor generates a median heart rate (HR) score for the median heart rate at step 704.
  • the processor may generate the median HR score for the median heart rate similar to how the processor generates the initial HR score for the initial heart rate at step 410 of FIG. 3.
  • the processor may use a similar process to the sample process flow 600 shown in FIG. 5. Accordingly, the processor may identify a first level HR range, a second level HR range, etc. As described with reference to the sample process flow 600, the heart rate ranges may be based on a first level HR value, etc.
  • the processor may determine the median HR score based on which heart rate range the median heart rate falls under. As an illustration, the processor may assign a first HR score amount on determining that the median heart rate is within the first level HR range, a second HR score amount on determining that the median heart rate is within the second level HR range, etc.
  • the heart rate ranges and/or the heart rate score amounts used to generate the median HR score may be the same as the heart rate ranges and/or heart rate score amounts used to determine the initial HR score.
  • the processor may generate the median HR score for the median heart rate based on whether the median heart rate is within the first level HR range or the second level HR range used to generate the initial HR score.
  • the heart rate ranges and/or heart rate score amounts used to generate the median HR score may be different from the heart rate ranges and/or heart rate score amounts used to generate the initial HR score.
  • the processor may identify additional heart rate ranges for the median heart rate than for the initial heart rate.
  • the processor may assign different heart rate score amounts to generate the median HR score compared to the initial HR score.
  • Table 1 below illustrates one example of heart rate score amounts that can be used by the wearable cardiac defibrillator 200 in generating the initial HR score and the median HR score.
  • the heart rate ranges and the heart rate score amounts used to determine the initial HR score and the median HR score are the same. More specifically, the first level HR range is heart rates below 100 bpm, the second level HR range is heart rates greater than or equal to 100 bpm but less than the threshold used to identify ventricular tachycardia (VT), and the third level HR range is heart rates greater than or equal to the VT threshold.
  • the first level HR range is heart rates below 100 bpm
  • the second level HR range is heart rates greater than or equal to 100 bpm but less than the threshold used to identify ventricular tachycardia (VT)
  • the third level HR range is heart rates greater than or equal to the VT threshold.
  • the processor is configured to assign a heart rate score amount of 0 if the initial or median heart rate is in the first level HR range (below 100 bpm), assign a heart rate score amount of 1 if the initial or median heart rate is in the second level HR range (greater than or equal to 100 bpm but less than the VT threshold), or assign a heart rate score amount of 2 if the initial or median heart rate is in the third level HR range (greater than or equal to the VT threshold).
  • Table 1 Example heart rate score amounts [0118]
  • Table 2 illustrates another example of heart rate score amounts that can be used by the wearable cardiac defibrillator 200 in generating the initial HR score and the median HR score.
  • the heart rate ranges used to determine the initial HR score and the median HR score are the same. Specifically, the first level HR range is less than 100 bpm, the second level HR range is greater than or equal to 100 bpm but less than 180 bpm, and the third level HR range is greater than or equal to 180 bpm.
  • the processor is configured to assign different heart rate score amounts depending on whether the processor is generating the initial HR score or the median HR score.
  • the processor is configured to assign a heart rate score amount of 0 if the initial or median heart rate is in the first level HR range (less than 100 bpm) or assign a heart rate score amount of 1 if the initial or median heart rate is in the second level HR range (greater than or equal to 100 bpm but less than 180 bpm). If the initial heart rate is in the third level HR range (greater than or equal to 180 bpm), the processor is configured to assign a heart rate score amount of 2, but if the median heart rate is in the third level HR range, the processor is configured to assign a heart rate score amount of 3.
  • Table 3 below illustrates another example of heart rate score amounts that can be used by the wearable cardiac defibrillator 200 in generating the initial HR score and the median HR score.
  • the heart rate ranges used to determine the initial HR score and the median HR score are once again the same, but the heart rate scores used for the initial heart rate and the median heart rate are different.
  • the first level HR range is less than 120 bpm and the second level HR range is greater than or equal to 120 bpm.
  • the processor is configured to assign a heart rate score amount of 10 if the initial heart rate is within the first level HR range (less than 120 bpm) or 0 if the initial heart rate is within the second level HR range (greater than or equal to 120 bpm). Conversely, the processor is configured to assign a heart rate score amount of 20 if the median heart rate is within the first level HR range or 0 if the initial heart rate is within the second level HR range.
  • Table 4 below illustrates another example of heart rate score amounts that can be used by the wearable cardiac defibrillator 200 in generating the initial HR score and the median HR score.
  • the heart rate ranges and the heart rate scores used to determine the initial HR score and the median HR score are different.
  • the first level HR range for the initial and median heart rates is less than 100 bpm and the second level HR range for the initial and median heart rates is less than or equal to 100 bpm but less than the VT threshold.
  • a third level HR range is greater than or equal to the VT threshold.
  • a fourth level HR range and a fifth level HR range are instead used for the median heart rate, with the fourth level HR range being less than or equal to the VT threshold but less than a threshold used to identify ventricular fibrillation (VF) and the fifth level HR range being greater than or equal to the VF threshold.
  • the processor is configured to assign a heart rate score amount 0 if the respective heart rate is within the first level HR threshold (less than 100 bpm) or assign a heart rate score amount of 1 if the respective heart rate is within the second level HR threshold (greater than or equal to 100 bpm but less than the VT threshold).
  • the processor is configured to assign a heart rate score amount of 2 if the initial heart rate is within the third level HR threshold (greater than or equal to the VT threshold). Conversely, for the median heart rate, the processor is configured to assign a heart rate score amount of 2 if the median heart rate is within the fourth level HR threshold (greater than or equal to the VT threshold but less than the VF threshold) or assign a heart rate score amount of 3 if the median heart rate is within the fifth level HR threshold (greater than or equal to the VF threshold).
  • Table 4 Example heart rate score amounts
  • the processor is configured to sum the initial HR score and the median HR score at step 706.
  • the sum of the initial HR score and the median HR score represents the arrhythmia verification HR score.
  • the arrhythmia verification HR score may generated based on some modifications to the initial HR score and the median HR score before and/or after the initial HR score and the median HR score are summed. For example, the processor may assign weights to the initial HR score and the median HR score to prioritize one or the other of the initial HR score and the median HR score in the sum.
  • the processor may compare the arrhythmia verification HR score to a predetermined heart rate (HR) score threshold at step 412.
  • HR heart rate
  • the predetermined HR score threshold may be user-configurable or programmable. For example, the patient's physician or a technician for the wearable cardiac defibrillator 200 may be able to increase the predetermined HR threshold if the wearable cardiac defibrillator 200 frequently outputs alerts that the patient 202 is experiencing a treatable arrhythmia condition when the patient 202 is still conscious.
  • the processor may compare the arrhythmia verification HR score to the predetermined HR score threshold to determine whether the arrhythmia verification HR score transgresses (e.g., is greater than, is greater than or equal to, is lesser than, or is lesser than or equal to) the predetermined HR score threshold.
  • the predetermined HR score threshold may be a default threshold (e g., a default threshold used by all wearable cardiac defibrillators 200).
  • the processor may determine whether the sum of the initial HR score and the median HR score is greater than a predetermined HR score threshold of 2. As another illustration, referring to the example of Table 2 above, the processor may determine whether the sum of the initial HR score and the median HR score is greater than or equal to a predetermined HR score threshold of 4. As another illustration, referring to the example of Table 3 above, the processor may determine whether the sum of the initial HR score and the median HR score is less than a predetermined HR score threshold of 20.
  • the processor may determine whether to initiate a treatment sequence based at least partially on the comparison of the arrhythmia verification HR score to the predetermined HR score threshold at step 414.
  • the processor in response to determining that the arrhythmia verification HR score transgresses the predetermined HR score threshold at step 412, the processor is configured to declare that the patient 202 is experiencing the arrhythmia condition. As such, the processor is configured to initiate the treatment sequence if the arrhythmia verification HR score transgresses the predetermined HR score threshold.
  • the treatment sequence may include initiating an alarm sequence to alert the patient 202 that the wearable cardiac defibrillator 200 has detected the arrhythmia condition occurring in the patient 202 and is preparing to deliver one or more treatment shocks to the patient 202.
  • the wearable cardiac defibrillator 200 may begin charging capacitors in preparation for delivering the one or more treatment shocks.
  • the treatment sequence is discussed in further detail above with respect to FIG. 1 and below with respect to FIG. 20.
  • the processor in response to determining that the arrhythmia verification HR score does not transgress the predetermined HR score threshold at step 412, the processor is configured to delay declaring that the patient 202 is experiencing the arrhythmia condition and initiating the treatment sequence.
  • the processor may implement one or more verification periods as described above with reference to FIG. 2. During the one or more verification periods, the processor may determine more heart rates for the patient 202. The processor may then use those heart rates to generate one or more additional arrhythmia verification scores that the processor analyzes to determine whether to initiate the treatment sequence. As such, in embodiments, the processor may perform a process flow similar to the process flow described with respect to steps 408, 410. 412, and 414 above.
  • the processor may determine one or more further heart rates similarly to the processes described above with reference to step 408.
  • the one or more further heart rates may be subsequent to the initial heart rate and the one or more additional heart rates determined at step 408.
  • the processor may determine at least a third heart rate. This at least third heart rate is subsequent to both the initial heart rate and at least one second heart rate determined during the first period of time following the initial determination of the arrhythmia condition (e.g., during the countdown period).
  • the processor may then generate a second arrhythmia verification HR score, such as at the expiration of the verification period, similarly to the processes described above with reference to step 410 and FIG. 6. Similar to the first arrhythmia verification HR score generated at step 410, the second arrhythmia verification HR score may be based on the one or more further heart rates determined during the verification period. In implementations, to generate the second arrhythmia verification HR score, the processor may calculate a second median heart rate from a second number of past heart rates, such as a certain number of heart rates most recently recorded for the patient 202. The second number of past heart rates may include a predetermined number of consecutive heart rates (e.g., between 2 and 200, between 5 and 20, etc., inclusive).
  • the second number of past heart rates may include the one or more further heart rates determined during the verification period (e.g., at least a third heart rate).
  • the second number of past heart rates may further include the one or more additional heart rates determined at step 408 (e.g., at least a second heart rate).
  • the second number of past heart rates may further include the initial heart rate.
  • the processor may then generate a second median HR score for the second median heart rate, for example, using the processes described above with reference to step 704 of FIG. 6.
  • the processor may generate the second arrhythmia verification HR score using the initial HR score and the one or more further heart rates. For example, the processor may sum the initial HR score and the second median HR score generated for the second median heart rate to generate the second arrhythmia verification HR score.
  • the initial HR score may be the initial HR score generated at step 406 of FIG. 3.
  • the processor may update the initial HR score before generating the second arrhythmia verification HR score. To illustrate, in implementations, the processor may update the initial HR score based on a current heart rate associated with the generation of the first arrhythmia verification HR score at step 410 of FIG. 3.
  • the processor may identify the patient's current heart rate at the point the first arrhythmia verification HR score was generated (e.g., the patient’s current heart rate at point 306 of the example timeline 300). The processor may then generate an updated initial HR score using the identified current heart rate. For instance, the processor may generate the updated initial HR score using the processes described above with respect to step 406 of FIG. 3. The processor may then generate the second arrhythmia verification HR score using the updated initial HR score and the one or more further heart rates. For example, the processor may sum the updated initial HR score and the second median HR score generated for the second median heart rate to generate the second arrhythmia verification HR score. In implementations, the initial HR score (e.g.. original or updated) and the second median HR score may be modified, such as with weights, before they are summed to generate the second arrhythmia verification HR score.
  • the initial HR score e.g.. original or updated
  • the second median HR score may be modified, such as with weights, before they are summed to generate the second arrhythm
  • the processor may compare the second arrhythmia verification HR score to the predetermined HR score threshold, similar to the process described with respect to step 412. Then the processor may again determine whether to initiate the treatment sequence based on the comparison, similar to the process described with respect to step 414. For example, in response to determining that the second arrhythmia verification HR score transgresses the predetermined HR score threshold, the processor may initiate the treatment sequence. Conversely, in response to determining that the second arrhythmia verification HR score does not transgress the predetermined HR score threshold, the processor may delay the treatment sequence or drop the initial determination of the arrhythmia condition.
  • the processor may drop the initial determination of the arrhythmia condition and return to monitoring the patient’s ECG signals for a treatable arrhythmia if the second arrhythmia verification HR score does not transgress the predetermined HR score threshold.
  • the processor may once again perform a process similar to steps 408, 410, 412, and 414 described above to implement another verification period of time, determine more heart rate(s) for the patient, generate a third arrhythmia verification HR score, compare the third arrhythmia verification HR score to the predetermined threshold, and determine whether to initiate the treatment sequence based on the comparison.
  • the processor may continue to implement further verification periods if the arrhythmia verification HR score does not transgress the predetermined HR score threshold until, for example, the processor has reached a certain number of arrhythmia verification HR scores generated (e.g., three, four, five, six, etc.) or a predetermined amount of time has elapsed (e.g., 60 seconds, 90 seconds, 120 seconds, 150 seconds. 180 seconds, etc.). At that point, the processor may make a final decision on whether to initiate the treatment sequence or drop the initial determination of the arrhythmia condition.
  • a certain number of arrhythmia verification HR scores generated e.g., three, four, five, six, etc.
  • a predetermined amount of time e.g. 60 seconds, 90 seconds, 120 seconds, 150 seconds. 180 seconds, etc.
  • FIG. 7 illustrates another sample process flow for making an initial determination of an arrhythmia condition and using the HR score to verify the initial determination of the arrhythmia condition.
  • the sample process 800 shown in FIG. 7 can be implemented by one or more processor of a wearable cardiac defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200.
  • Other embodiments of wearable cardiac defibrillators, including the other embodiments described herein, may similarly implement the sample process 800.
  • the processor is configured to monitor the patient’s ECG signals at step 802.
  • the processor is configured to determine whether the patient is experiencing an arrhythmia condition at step 804.
  • the processor may perform steps 802 and 804 using the process flow 500 of FIG. 4. If the processor determines that the patient is experiencing the arrhythmia condition, the processor records an initial determination of the arrhythmia condition at step 806. Otherwise, the processor returns to monitoring the patient’s ECG signals at step 802.
  • the processor identifies an initial heart rate associated with the initial determination at step 808.
  • the processor may perform step 806 similarly to the processes of step 404 discussed above with reference to FIG. 3.
  • the processor then generates an initial heart rate score at step 810.
  • the processor may perform step 810 similarly to the processes of step 406 discussed above with reference to FIG. 3 and the sample process 600 discussed above with reference to FIG. 5.
  • the processor determines one or more additional heart rates at step 812, such as during a countdown or verification period.
  • the processor may perform step 812 similarly to the processes of step 408 discussed above with reference to FIG. 3.
  • the processor generates an arrhythmia verification HR score at step 814, such as by using the initial heart rate score determined at step 810 and the one or more additional heart rates determined at step 812.
  • the processor performs step 814 similarly to the processes of step 410 discussed above with reference to FIG. 3 and the sample process 700 discussed above with reference to FIG. 6.
  • the processor determines whether the arrhythmia verification HR score transgresses a predetermined HR score threshold at step 816. In implementations, the processor compares the arrhythmia verification HR score to the predetermined HR score threshold to determine whether the arrhythmia verification HR score transgresses the predetermined HR score threshold similarly to the processes of step 412 described above with reference to FIG. 3. If the arrhythmia verification HR score does not transgress the predetermined HR score threshold at step 816, the processor determines whether the processor has made a maximum number of attempts to verify the initial determination of the arrhythmia condition at step 817. For example, the processor may determine whether the processor has implemented a maximum number of countdown/verification periods.
  • the processor may determine whether a maximum amount of time to verify the initial determination of the arrhythmia condition has passed. As another example, the processor may determine whether the processor has generated a maximum number of arrhythmia verification HR scores. If the processor has made a maximum number of verification attempts at step 817, the processor may drop the initial determination of the arrhythmia condition at step 819. The processor may then resume monitoring the patient's ECG signals at step 802.
  • the processor identifies the current heart rate at step 818. For instance, in implementations and as described above, the processor identifies the current heart rate for the point at which the arrhythmia verification HR score was generated (e.g., the patient’s current heart rate at step 814). The processor may then update initial heart rate score at step 820. In implementations, the processor may re-perform step 810 but using the current heart rate identified at step 818 (e.g., again using the processes of step 406 discussed above with reference to FIG. 3 and the sample process 600 discussed above with reference to FIG. 5). The processor then again performs steps 812, 814, and 816. such as during a verification period. If instead at step 816 the processor determines that the arrhythmia verification HR score does transgress the predetermined HR score threshold, the processor confirms the initial determination of the arrhythmia condition and initiates a treatment sequence at step 822.
  • the wearable cardiac defibrillators described herein may also increase the accuracy of identifying that the patient is experiencing a treatable cardiac arrhythmia, and decrease the number of false alarms, through improved processes for determining the patient’s heart rates. With a more accurate heart rate, it is less likely that the wearable cardiac defibrillator will incorrectly detect that the patient is experiencing a treatable arrhythmia.
  • these improved processes may include systems and methods for better identification of the patient’s R peaks in their ECG signals. As R-R intervals are often used to determine heart rate, by improving how the R peaks are identified, the determination of the patient’s heart rate may likewise be improved.
  • the wearable cardiac defibrillator may be configured to determine the initial heart rate and one or more additional heart rates (e.g., at least a second heart rate) based on detected R peaks in the patient’s ECG signals.
  • the wearable cardiac defibrillator may detect the R peaks in the patient’s ECG signals using a separation threshold, where the separation threshold is configured to separate R peaks from other portions of the ECG signals. Accordingly, when a part of the patient’s ECG signals crosses the separation threshold, the wearable cardiac defibrillator classifies the part crossing the separation threshold as an R peak.
  • the earable cardiac defibrillator may use a QRS detector to identify R peaks in the patient’s ECG signals. As part of employing the QRS detector, the wearable cardiac defibrillator may output filtered, smoother ECG signals from ECG data points generated from the patient’s sensed electrical cardiac activity.
  • the QRS detector may then identify peaks in the filtered ECG signals as R peaks when the peaks cross the separation threshold and determine the distance between successive identified R peaks to measure an R-R interval of the filtered ECG signals.
  • the wearable cardiac defibrillator may then use the R-R interval to determine the patient’s heart rate.
  • the QRS detector may estimate the separation threshold based on statistics of the ECG signals (e.g., a maximum amplitude, an average amplitude, etc. of the filtered ECG signals).
  • the QRS detector may additionally or alternatively determine where false R peaks have been identified in the ECG signals after applying the separation threshold to the filtered ECG signals (e.g., where the false R peaks correspond to the T wave, the P wave, noise, etc.). The QRS detector may then discard the false peaks before measuring the R-R intervals.
  • the wearable cardiac defibrillator may additionally or alternatively predict an optimal separation threshold before applying the separation threshold to identify R peaks in the filtered ECG signals.
  • Such a process may increase the accuracy of the QRS detector, while eliminating or decreasing the need to identify' falsely identified R peaks in the ECG signals.
  • it may be beneficial to periodically update the separation threshold to reflect the current ECG signals for the patient.
  • the sensing electrodes may shift on the patient’s body over time such that the amplitude and/or noise level of the ECG signals generated from the patient’s sensed electrical cardiac activity changes over time.
  • the noise level of the ECG signals may increase such that more peaks of the ECG signals created from noise are crossing the separation threshold and being falsely classified as R peaks.
  • the overall amplitude of the ECG signals may decrease such that fewer R peaks of the ECG signals are crossing the separation threshold. Thus, these R peaks not crossing the separation threshold are not being classified as R peaks.
  • the patient’s ECG signals may change over time, such as due to changes in the patient’s cardiac health. These changes in the patient’s ECG signals may similarly result in under- or over-classification of R peaks in the patient’s ECG signals.
  • the wearable cardiac defibrillator may be configured to set the separation threshold using local maxima of the patient’s ECG signals.
  • FIG. 8 illustrates a sample process flow for using the local maxima in a segment of the patient's ECG signals to determine a separation threshold, where the separation threshold is later used to identify R peaks for the patient.
  • the sample process 900 shown in FIG. 8 can be implemented by one or more processors of a wearable defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200.
  • the processor is configured to filter the patient's ECG signals at step 902.
  • the filtering is configured to smooth out the patient’s ECG signals compared to the raw ECG data generated from the sensed electrical activity of the patient 202.
  • the filtering may also provide for signals with better differentiation between signal peaks and nonpeak portions of the filtered ECG signals compared to the non-filtered ECG signals and/or correct negative amplitudes of the unfiltered ECG signals (e.g., change the amplitude of the R waves of the unfiltered ECG signals to be positive if they are negative).
  • the processor may not filter the ECG signals and instead apply the sample process 900 to unfiltered ECG signals. As such, the remaining steps of the sample process 900 may instead be applied to an unfiltered ECG signal.
  • the processor may identify a segment of the ECG signals to analyze using the proceeding steps of the sample process 900. In implementations, the processor may identify a segment of the ECG signals to analyze before filtering just the identified segment at step 902.
  • the processor is configured to identify local maxima in the filtered ECG signals at step 904.
  • the processor identifies local maxima by comparing each sample point of the filtered ECG signals with an immediately previous sample point of the filtered ECG signals. If the amplitude of the current sample point is greater than the amplitude of the immediately previously sample point, no local maxima is identified. If the amplitude of the current sample point is less than the amplitude of the immediately previous sample point, the processor identifies the immediately previous sample point as a local maximum.
  • the processor may identify the local maxima by determining the derivative of the filtered ECG signals. Where the derivative of the filtered ECG signals is zero, the processor may identify’ the corresponding point of the filtered ECG signals as being a local maximum.
  • the processor may, in various examples, store the local peak values and positions in a running circular buffer as they are found.
  • the processor applies one or more clustering analyses to classify each of the identified local maxima as a true peak or a false peak at step 906.
  • the true peak corresponds to a predicted R peak of the ECG signals (e g., a peak that the processor suspects is an R peak)
  • the false peak corresponds to a predicted other peak of the ECG signals (e.g., a peak that the processor suspects is not an R peak, such as the peak of a P wave, the peak of a T wave, a peak due to noise, etc.).
  • the processor may apply a first and a second clustering step to the ECG signals.
  • the processor may compare each identified local maximum with the previous identified local maximum. If the value of the current local maximum is less than the value of the previous local maximum, the processor labels the current local maximum as a false peak. If the value of the current local maximum is greater than or about equal to (e.g., within 5% of, 10% of, within 15% of, etc.) the previous local maximum, the processor labels the current local maximum as a true peak. In examples, the processor may store these classifications of the local maxima as true or false peaks in a running circular buffer. As another example, the processor may additionally or alternatively apply a similar comparison analysis for each local maximum and the subsequent local maximum. IF the value of the current local maximum is greater than or about equal to the subsequent local maximum, the processor labels the current local maximum as a true peak.
  • FIG. 9 illustrates an example of using a clustering step on an ECG signal 950 to classify local maxima identified at step 904 as true and false peaks.
  • FIG. 9 shows that local maxima 952 have been identified in the ECG signal 950.
  • the processor has labeled the local maxima 954 as true peaks.
  • the processor has labeled the remaining local maxima 952 as false peaks 958.
  • the processor can also calculate an updated separation threshold 956 based on the true peaks 954 (e.g., by determining the average between the threshold of the lowest amplitude true peak 954a and the highest amplitude false peak 958a).
  • comparing the amplitude of each of the local maxima 952 to the amplitude of the previous local maximum may be enough to calculate an updated separation threshold 956 that accurately detects R peaks in the ECG signal 950.
  • the updated separation threshold 956 only results in one R-peak detection per QRS complex.
  • the processor may only use one clustering step to classify true and false peaks in the ECG signal 950.
  • applying this single clustering step may not be enough to calculate an updated separation threshold 956 and multiple clustering steps may be applied.
  • the processor may compare the amplitudes of the true peaks from the first clustering step to each other to determine, for example, whether any of the true peaks from the first clustering step is an incorrectly classified T wave.
  • the processor may calculate a left derivative and a right derivative betw een each identified true peak and the previous and subsequent identified true peak, respectively.
  • the processor may determine a slope between the given true peak and the previous true peak as the left derivative and a slope between the given true peak and the subsequent true peak as a right derivative.
  • the processor then normalizes the left and right derivatives (e.g., using the average value of the positive peaks).
  • the processor compares the normalized left derivative to a minimum negative threshold and the normalized right derivative to a maximum positive threshold. If the left derivative is less than the minimum negative threshold and the right derivative is greater than the maximum positive threshold, the processor relabels the identified true peak as a false peak. In this way, the processor can control, for example, for peaks produced by P waves and T waves that may be incorrectly labeled as true peaks.
  • the processor may be configured to compare the amplitude of the true peak with the amplitude of the previous identified true peak and the amplitude of the subsequent identified true peak. Based on this comparison, the processor may determine whether the amplitudes of the three peaks are similar (e.g., within a certain percentage or normalized amount of each other, such as within 5%, 10%, 15%, etc.). If the three peaks are similar, the identified true peak remains a true peak.
  • the processor reclassifies the peak as a false peak.
  • the processor may compare the amplitude of the true peak with the amplitude(s) of false peak(s) near the given true peak (e.g.. between the given true peak and the previous and/or subsequent identified true peak). If the true peak is correctly identified, the amplitude of the true peak should typically be greater than the amplitude(s) of the adjacent false peak(s).
  • the processor determines that the amplitude of the true peak is greater than the amplitude(s) of the false peak(s) (e.g., at least a certain percentage or normalized amount greater, such as greater than 10%, 15%, 20%, 25%, etc.), the identified true peak remains a true peak. If instead the processor determines that the amplitude of the true peak is around the same as the amplitude(s) of the adjacent false peak(s), the processor may reclassify the true peak as a false peak.
  • the processor may determine whether the amplitude of the given true peak is greater than a representative amplitude of nearby false peaks (e.g., immediately adjacent false peaks, false peaks between the given true peak and the previous and/or subsequent true peak, a certain number of false peaks nearby in time to the given true peak, etc.). For instance, the processor may determine an average, median, mode, etc. of the amplitudes of the nearby false peaks, which the processor compares to the amplitude of the given true peak. [0145] In implementations, the clustering process may include other ways to separate true peaks corresponding to predicted R waves from false peaks corresponding to other peaks in the ECG signals.
  • nearby false peaks e.g., immediately adjacent false peaks, false peaks between the given true peak and the previous and/or subsequent true peak, a certain number of false peaks nearby in time to the given true peak, etc.
  • the processor may determine an average, median, mode, etc. of the amplitudes of the nearby false peaks, which the processor compares
  • the processor may determine the distance between a given true peak identified in the first clustering step and the previous identified and/or subsequent identified true peak.
  • the processor may also determine the distance between the given true peak and the false peak(s) between the given true peak and the previous and/or subsequent true peak. If the given true peak is correctly identified, the true peak should show a greater distance between the previous and/or subsequent true peak than between the false peak(s) near the given true peak.
  • the processor may reclassify the given true peak as a false peak.
  • FIG. 10 illustrates an example of using a clustering process 1000 to separate true peaks from false peaks.
  • the processor may identify a series of local maxima 1002, 1004, 1006, 1008, and 1009 in an ECG signal 1010 (e.g., by applying step 904).
  • the processor may then classify local maxima 1002, 1004, and 1006 as true peaks in a first clustering step. For example, as discussed above, the processor may compare the amplitude of each local maximum to the amplitude of the previous local maximum and label local maxima with a higher amplitude than the previous local maximum as a true peak.
  • the processor classifies the rest of the local maxima 1008 and 1009 as false peaks.
  • the processor may verify the classification of the true peaks 1002, 1004, and 1006 and false peaks 1008 in a second clustering step. For instance, the processor may compare the amplitude of each true peak to the amplitudes of the surrounding false peaks to verify that the given true peak’s amplitude is greater than the amplitudes of the surrounding false peaks.
  • FIG. 10 demonstrates true peak 1002 being compared to the false peaks 1008 between the true peak 1002 and the next identified true peak 1004. The amplitude of true peak 1002 is greater than the amplitudes of the false peaks 1008, which helps confirm that the true peak 1002 was correctly identified.
  • the processor may compare the amplitudes of each true peak to the amplitudes of the surrounding true peaks to verify’ that the given true peak’s amplitude is similar to the amplitudes of the surrounding true peaks.
  • FIG. 10 shows the amplitude of the true peak 1004 being compared to the preceding identified true peak 1002 and the subsequent identified true peak 1006. As shown, the amplitudes of the identifies true peaks 1002, 1004, and 1006 are similar, which helps verify that the true peak 1004 is correctly labeled as a true peak.
  • the clustering process may be configured as a lightweight protocol. For instance, the processor may identify the local maxima by comparing each ECG signal sample point to the previous ECG signal sample point, as described above. The processor may then identify true peaks corresponding to suspected or predicted R waves by comparing each local maxima to the previous local maxima to classify which local maxima are true peaks and then demote the identified true peaks based on the left and right derivatives, as described above. Because the clustering process primarily involves comparing a series of points with each other, this clustering process may be configured as lightweight. As such, the clustering process may be less computationally intensive for a processor to run.
  • FIG. 11 illustrates an example of using a clustering process to classify each of the local maxima as true peaks in an ECG signal 1100.
  • the processor has identified a series of local maxima 1102, 1104, and 1106.
  • the processor classifies local maxima 1102 and 1104 as true peaks and local maxima 1106 as false peaks.
  • the processor compares the amplitude of each local maximum to the amplitude of the previous local maximum.
  • the processor classifies local maxima with higher amplitudes as true peaks (e.g., local maxima 1102 and 1104) and local maxima with lower amplitudes as false peaks (e.g., local maxima 1106).
  • the processor further reclassifies local maxima 1104 as false peaks and maintains local maxima 1 102 as true peaks.
  • the processor may calculate the left and rights derivatives with respect to the surrounding true peaks. The processor then compares the left derivative to a minimum negative threshold and the right derivative to a maximum positive threshold. For local maxima where the left derivative is less than the minimum negative threshold and the right derivative is greater than the maximum positive threshold (e.g., local maxima 1104), the processor reclassifies the local maxima as false peaks. For the remaining peaks (e.g...
  • the processor keeps the classification of true peaks from the first clustering step 1108. In this way, the processor does not misidentify peaks corresponding to T waves (e.g., local maxima 1 104) as peaks corresponding to R waves.
  • the result of the first and second clustering steps 1108 and 1110 is that the local maxima 1102, corresponding to R waves in the ECG signal 1100, are labeled as true peaks and the local maxima 1104 and 1106, corresponding to other peaks in the ECG signal 1100, are labeled as false peaks.
  • the processor is configured to use the classified true and false peaks to find a good separation threshold for the ECG signals.
  • the separation threshold is only effective if it can separate the true peaks corresponding to what the processor suspects are R waves from other waves in the ECG signals (e.g., if the amplitude of the smallest of the true peaks is larger than the amplitude of the greatest of the false peaks). If the amplitudes of the classified true and false peaks are too similar, the separation threshold will not be effective.
  • the processor may apply additional steps to determine (a) whether to set a dynamic separation threshold or use a default separation threshold and (b) if a dynamic separation threshold is to be set, what the dynamic separation threshold should be set as.
  • the processor is configured to calculate a margin between the classified true peak(s) and the classified false peak(s) at step 908.
  • the margin may be, for instance, the difference in amplitude between the minimum positive peak and the maximum negative peak.
  • the processor may additionally, in various examples, normalize the calculated margin at step 908. For instance, the processor may divide the margin by the amplitude of the smallest true peak.
  • the processor may also be configured to count the number of classified true peaks in the ECG signal segment at step 910.
  • the processor may be configured to perform steps 908 and 910 in any order, or the processor may be configured to perform only one of steps 908 or 910.
  • the processor can then use the margin calculated at step 908 and the counted true peaks at step 910 to determine the separation threshold that the processor will apply to the patient’s future ECG signals.
  • a positive margin may show that the R peaks can be separated from other peaks in the ECG signals using a dynamic separation threshold.
  • a negative margin may indicate a problem with one of the true peaks (e.g., due to noise or an issue with one or more of the sensing electrodes, such as sensing electrodes 204) or a chaotic ECG signal pattern that has created a misclassification (e.g., a shockable rhythm).
  • the processor may determine at step 912 whether the calculated margin from step 908, such as the normalized margin, is transgresses a minimum margin condition (e.g., whether the calculated margin is greater than the minimum margin condition, whether the calculated margin is greater than or equal to the minimum margin condition, etc.).
  • the minimum margin condition may be zero. In implementations, the minimum margin condition may be greater than zero. For example, a small, positive margin may additionally show that the true and false peaks have a similar amplitude (e.g., are falling in an oscillation pattern). In such cases, the ECG segment being used may similarly represent, for example, a shockable rhythm or the presence of noise.
  • the processor may thus use a minimum positive margin condition. In implementations, if the processor determines that the calculated margin does not transgress the minimum margin condition, the processor may set the separation threshold to a predetermined or default separation threshold.
  • the processor may additionally determine at step 912 whether the calculated margin transgresses a maximum margin condition (e.g., whether the calculated margin is less than the maximum margin condition, whether the calculated margin is less than or equal to the maximum margin condition, etc.).
  • the processor may impose the maximum margin condition as a safety precaution, such as to prevent the processor from using a dynamic separation threshold where the patient’s ECG signals are weak with large peaks produced by noise.
  • the processor may similarly use a default separation threshold where the calculated margin transgresses the maximum margin condition.
  • the processor may determine at step 912 whether the counted number of true peaks is greater than a predetermined true peak number. For example, the processor may determine whether the counted number of true peaks is greater than one. As another example, the processor may determine whether the counted number of true peaks is greater than another integer, such as two, three, four, five, etc. If the counted number of true peaks is not greater than the predetermined true peak number, it may be indicative of the processor not accurately classifying true peaks, i.e., not identifying all of the R peaks in the ECG signal segment. Accordingly, the processor may set the separation threshold as a predetermined or default separation threshold in such cases.
  • the processor may use a dynamic separation threshold at step 912. For instance, the processor may use the dynamic separation threshold (a) if the calculated margin from step 908 transgresses the minimum margin condition, (b) if the calculated margin from step 908 does not transgress the maximum margin condition, and (c) if the counted number of true peaks is greater than the predetermined true peak number. As another example, the processor may use the dynamic separation threshold if the true peaks in the ECG signal segment satisfy two of the three above conditions. As another example, the processor may use fewer or additional predetermined conditions.
  • the processor may only determine whether the calculated margin from step 908 transgresses the minimum margin condition and/or does not transgress the maximum margin condition. As another illustration, the processor may only determine whether the number of classified true peaks is greater than a predetermined true peak number at step 910.
  • the processor may further determine an updated separation threshold for use in detecting R peaks in the patient’s future ECG signals.
  • the processor may calculate the updated separation threshold based on amplitudes of the true peaks classified at step 908. For example, the processor may calculate the updated separation threshold as an average value between the amplitude of the smallest classified true peak and the amplitude of the largest classified false peak. As another example, the processor may calculate the updated separation threshold as a predetermined amount below the average value of the true peaks, such as 75% of the average amplitude of the true peaks. As another example, the processor may calculate the updated separation threshold as a predetermined amount below the largest false peak, such as 125% of the amplitude of the largest false peak.
  • FIG. 12 illustrates another sample process flow for using the local maxima in a segment of the patient’s ECG signals to determine a separation threshold.
  • the sample process 1200 shown in FIG. 12 can be implemented by one or more processor of a wearable cardiac defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200.
  • Other embodiments of wearable cardiac defibrillators, including the other embodiments described herein, may similarly implement the sample process 1200.
  • the processor is configured to filter the patient’s ECG signals at step 1202.
  • the processor may perform step 1202 similarly to the processes of step 902 discussed above with reference to FIG. 8.
  • the processor may filter the patient’s ECG signals as part of performing signal processing on sensed electrical cardiac activity' of the patient’s heart.
  • the processor is configured to identify an ECG segment at step f204.
  • the processor may determine that it is time to carry out the sample process 1200 and determine an updated separation threshold, for example, based on a predetermined amount of time elapsing since the last time the sample process 1200 was performed or based on a cardiac event happening in the patient (e.g., a cardiac arrhythmia being detected in the patient).
  • the processor may perform the sample process 1200 when the patient is first fitted for the wearable cardiac defibrillator.
  • the processor may perform the sample process 1200 on a periodic basis, such as once an hour, once every 2 hours, once every 4 hours, once every 6 hours, once every' 8 hours, once every 12 hours, once every' 24 hours, once every 48 hours, once every 72 hours, once a week, etc.
  • the processor may perform the sample process 1200 in response to the wearable cardiac defibrillator detecting that the patient is experiencing a cardiac arrhythmia.
  • the processor may perform the sample process 1200 in response to a false detection that the patient is experiencing a cardiac arrhythmia.
  • the processor may identify an ECG segment corresponding to the point the processor determines it is time to perform the sample process 1200.
  • the processor may identify an ECG segment of predetermined length (e.g., a six-second ECG segment, a ten-second ECG segment, a twelve-second ECG segment, etc.) starting at the point the processor starts performing the sample process 1200.
  • the processor may determine an updated separation threshold frequently (e.g., once every 5 minutes, once every 10 minutes, once every' 20 minutes, once every' 30 minutes, once an hour, once every 2 hours, once every 3 hours, etc ), provided that the conditions for an updated separation threshold as discussed herein are met (e.g., peaks separability'), due to the lightweight signal processing needed to determine the updated separation threshold.
  • an updated separation threshold frequently (e.g., once every 5 minutes, once every 10 minutes, once every' 20 minutes, once every' 30 minutes, once an hour, once every 2 hours, once every 3 hours, etc )
  • the processor identifies local maxima in the ECG segment at step 1206.
  • the processor may perform step 1206 similarly to the processes of step 904 discussed above with reference to FIG. 8.
  • the processor applies one or more clustering analyses to classify' local maxima as true or false peaks at step 1208.
  • the processor may perform step 1208 similarly to the processes of step 906 discussed above with reference to FIG. 8.
  • the processor then calculates a margin between the true and false peaks at step 1210 and counts the number of true peaks at step 1216.
  • the processor may perform step 1210 similarly to the processes of step 908 and may perform step 1216 similarly' to the processes of step 910 discussed above with reference to FIG. 8.
  • the processor determines whether the margin between the true and the false peaks transgresses a minimum margin condition at step 1212. In implementations, the processor may also determine whether the margin between the true and false peaks transgresses a maximum margin condition as part of step 1212. The processor also determines whether the number of true peaks is greater than one (or another predetermined true peak number) at step 1218.
  • the processor determines that the margin between the true and the false peaks does not transgress the minimum margin condition at step 1212 (and, in implementations, does not transgress the maximum margin condition) and/or if the processor determines that the number of true peaks is not greater than one (or another predetermined true peak number) at step 1218, the processor sets the separation threshold to a predetermined or default separation threshold at step 1220. The processor then uses the default separation threshold to detect R peaks in the patient’s ECG signals (e.g., until the processor reruns the sample process 1200).
  • the processor determines that the margin between the true and the false peaks transgresses the minimum margin condition at step 1212 and that the number of true peaks is greater than one at step 1218. If the processor instead determines that both conditions are satisfied at step 1222. In response to determining that both conditions are satisfied, the processor calculates an updated separation threshold at step 1224. The processor then sets the updated separation threshold for future R-peak detection at step 1226.
  • FIGS. 13A and 13B illustrate an example of using a dynamic separation threshold to identify R peaks in an ECG signal 1300.
  • the wearable cardiac defibrillator detects several ECG channels, including a side-to-side ECG signal 1302 and a front-back ECG signal 1304.
  • the side-to-side ECG signal 1302 may be generated using sensing electrodes 204b and 204d positioned on the patient’s sides.
  • the front-back ECG signal 1304 may be generated using sensing electrodes 204a and 204c positioned on the patient’s front torso and back torso.
  • the wearable cardiac defibrillator may filter the ECG signals 1302 and 1304 as discussed above with reference to sample process 900 and sample process 1200 to generate a filtered side-to-side ECG signal 1306 and a filtered front-back ECG signal 1308.
  • FIGS. 13A and 13B further show a default separation threshold 1310 and a dynamic separation threshold 1312 applies to the ECG signals 1302, 1304, 1306, and 1308.
  • the default separation threshold 1310 may result in misclassification of peaks in the ECG signals 1306 and 1308 as R peaks.
  • the wearable cardiac defibrillator is able to more accurately classify peaks in the ECG signals 1306 and 1308 as R peaks.
  • the wearable cardiac defibrillator is able to more accurately calculate the patient’s heart rate (e.g., using the average time period between identified R peaks over an ECG segment).
  • FIGS. 13A and 13B illustrate R-peak detections with respect to the unfiltered ECG signals 1302 and 1304.
  • the R-peak detections 1314 using the default separation threshold 1310 show multiple R-peak detections per ECG wave
  • the R-peak detections 1316 using the dynamic separation threshold 1312 show one R-peak detection per ECG wave.
  • the R-peak detections 1314 and 1316 confirm that the wearable cardiac defibrillator can more accurately detect a single R wave per QRS complex in the ECG signals 1302 and 1304.
  • the wearable cardiac defibrillator may additionally or alternatively implement methods for improving the accuracy of heart rate calculations given R-peak detections.
  • An example method for improving the accuracy of heart rate calculations may involve preparing histograms of intervals between R-peak detections as a way to avoid incorrectly calculating the patient’s heart rate if the R-peak detections include one or more false detections (e g., due to T waves. P waves, and/or noise or other artifacts in the patient’s ECG signals).
  • the wearable cardiac defibrillator may calculate ECG intervals between R-peak detections and sort the intervals into a series of histogram bins.
  • the wearable cardiac defibrillator may estimate the patient’s heart rate.
  • the histogram bin with the greatest magnitude is a guard bin (e.g., implemented to catch ECG intervals caused by noise or other artifacts)
  • the wearable cardiac defibrillator may use a default method of determining the patient’s heart rate. For instance, the wearable cardiac defibrillator may default to determining the patient’s heart rate based on the average interval between a predetermined number of consecutive detected R peaks.
  • FIG. 14 illustrates a sample process flow for sorting ECG intervals into histogram bins to determine the patient’s heart rate.
  • the sample process 1400 shown in FIG. 14 can be implemented by one or more processors of a wearable defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200.
  • Other embodiments of wearable cardiac defibrillators, including the other embodiments described herein, may similarly implement the sample process 1400.
  • the processor is configured to calculate the patient’s ECG intervals based on R-peak detections at step 1402.
  • the processor may retrieve a predetermined number of R-peak detections or R-peak detections over a predetermined time interval.
  • the R-peak detections may be the result of the processor using a default or dynamic separation threshold based on the results of sample process 900 and/or 1200, as described above.
  • the processor may then determine the ECG intervals as the intervals between some or all of the identified R-peak detections.
  • the processor may retrieve the timings for an m number of R-peak detections, starting with the most recent R-peak detection and moving back consecutively and chronologically to identify R-peak detections until the m number of detections is reached.
  • m may be a predetermined number of consecutive R-peak detections, such as 10, 15, 20, 25, etc. R-peak detections. Accordingly, if the most recent R-peak detection is a, the processor may identify the relevant R-peak detections a. a - 7, a - 2, a - 5. and so on. until the processor identifies a - m.
  • the processor may calculate an interv al between the given R peak and the previous consecutive R peaks going back an n number of R-peak detections within the set of m number of R peaks.
  • the n number may be a predetermined number that is equal to or less than m. such as 5, 10, 15, 20, etc. Both m and n may be selected, for example, to balance efficiency in carrying out the sample process 1400 and ensuring that the sample process 1400 is responsive to changes in the patient's heart rate with safeguarding that the process 1400 uses a robust sample of R-peak detections in determining the patient’s heart rate (e.g., to avoid over-characterizing the patient’s heart rate when noise or spurious R-peak detections are present). Additionally, in implementations, the R-peak detections may be stored in a ring buffer such that the processor can calculate the histogram results on request using the current values in the ring buffer.
  • FIG. 15 illustrates an example of calculating ECG intervals based on R-peak detections.
  • a timeline 1500 including a number of R-peak detections 1502a-j (collectively referred to herein as 1502), with 1502a being the most recent R-peak detection and 1502j being the oldest retrieved R-peak detection.
  • the m number of detections used by the processor is ten in the example timeline 1500.
  • the processor calculates the interval between the given R-peak detection 1502 and the previous n number R-peak detections 1502 (with n being five in the example of FIG.
  • the processor calculates the intervals between R-peak detection 1502a and the five previous, consecutive R-peak intervals 1502b, 1502c, 1502d, 1502e, and 1502f. This results in the processor calculating (1) ECG interval 1504a between R-peak detections 1502a and 1502b.
  • ECG interval 1504b between R-peak detections 1502a and 1502c
  • ECG interval 1504c between R-peak detections 1502a and 1502d
  • ECG interval 1504d between R-peak detections 1502a and 1502e
  • ECG interval 1504e between R-peak detections 1502a and 1502f, as shown.
  • the processor repeats this process of calculating the ECG intervals with the five previous R-peak detections 1502 for R-peak detections 1502b-e.
  • the processor only calculates the ECG intervals with the remaining R-peak detections 1502 because fewer than five R-peak detections remain in the set of R-peak detections 1502.
  • the processor calculates (1) ECG interval 1506a between R-peak detections 1502g and 1502h, (2) ECG interval 1506b between R-peak detections 1502g and 1502i, and (3) ECG interval 1506c between R-peak detections 1502g and 1502j .
  • the processor does not calculate any ECG intervals since R-peak detection 1502j is the only remaining R-peak detection in the set of R-peak detections 1502.
  • the processor calculates 35 ECG intervals between the various R-peak detections 1502. Additionally.
  • FIG. 15 illustrates determining ECG intervals with respect to R-peak detections. However, in implementations, the process shown in FIG. 15 (as well as the processes described with respect to FIG. 14 and sample process flow 1400) may be applied to any cyclical portion of the patient's ECG signals.
  • the processor sorts the ECG intervals into histogram bins at step 1404.
  • Each histogram bin may correspond with a specific heart rate or range of heart rates.
  • the histogram bins may also include a guard bin intended to receive ECG intervals that are likely indicative of noise or other artifacts (e.g.. ECG intervals less than and/or greater than a predetermined length).
  • the processor determines the histogram bin with the greatest magnitude at step 1406.
  • the processor estimates the patient’s heart rate based on the histogram bin with the greatest magnitude at step 1408. For example, as noted above, each histogram bin may correspond with a specific heart rate or range of heart rates.
  • the processor may determine the patient’s heart rate as being a specific heart rate or a representative heart rate (e.g., an average of the heart rate range represented by the histogram bin) associated with the bin having the greatest magnitude. Otherwise, if the histogram bin with the greatest magnitude is the guard bin, the processor may instead use a default method of determining the patient’s heart rate. To illustrate, the processor may determine an average or median R-R interval of the sampled R-peak detections (e.g., the 10 R-peak detections) and use the average or median R- R interval to estimate the patient’s heart rate.
  • a representative heart rate e.g., an average of the heart rate range represented by the histogram bin
  • Table 5 below includes an example set of R-peak detection timings for an R-peak set is shown in Table 5, with the R1 peak being the most recent R-peak detection of the R-peak set (at timing 0.00 s) and the R10 peak being the most distant R-peak detection of the R-peak set (at timing 4.20 s).
  • Table 5 also includes the ECG intervals between the R peaks, as determined using the process outlined above and illustrated with respect to FIG. 15.
  • Table 5 includes ten R-peak detections, but in other examples, the number of R-peak detections used could be less than ten (e.g., 5 R-peak detections) or greater than 10 (e.g., 15, 20, 25, 50, 100, etc. R-peak detections).
  • the R-peak detection timings shown in Table 5 could be indicative of, for example, a heart rate of 100 bpm with two instances of false R-peak detection (R5 at 2.00 s and R9 at 3.80 s).
  • the processor can determine the ECG intervals as shown in Table 5 (e.g.. performing step 1402 from FIG. 14) and further sort the ECG intervals from Table 5 into a histogram (e.g., performing step 1404 from FIG. 14).
  • FIG. 16 shows an example histogram 1600 of the ECG intervals from Table 5.
  • the bin with the largest magnitude 1602 in the histogram 1600 occurs for the ECG interv al of 0.6 s, which corresponds to a heart rate of 100 bpm.
  • the processor may determine that the patient's heart 100 bpm (e.g.. performing steps 1406 and 1408 from FIG. 14).
  • the processor may confirm the patient’s heart rate being 100 bpm based on bin harmonics.
  • the processor may confirm the patient’s heart rate of 100 bpm based on the bins with the next largest magnitudes 1604, 1606, and 1608.
  • These next largest bins 1604, 1606, and 1608 occur for ECG intervals 1.2 s. 1.8 s. and 2.4 s, respectively. Given that 1.2 s, 1.8 s, and 2.4 s are multiples of 0.6 s, the processor may determine that these additional large bins 1604, 1606, and 1608 also evidence a heart rate of 100 bpm.
  • the R-peak detection timings show n in Table 5 may be more representative of ideal timings. In more realistic measurements of R peaks, there may be more variations and noise between the R-peak detections. As such, a more realistic example set of R-peak detections, still corresponding to a heart rate of 100 bpm with two false R-peak detections, is provided in Table 6 below. Similar to Table 5, Table 6 includes ten R-peak detections, with the R1 peak being the most recent R-peak detection (at timing -0.04 s) and the R10 peak being the most distant R-peak detection (at timing 4. 17 s).
  • the processor can determine the ECG intervals shown in Table 6 from the R-peak detection timings (e.g., performing step 1402 of FIG. 14). However, because the R-peak detection timings from Table 6 are less regular than the R-peak detection timings from Table 5, the associated ECG intervals are also less regular. For instance, instead of there being seven 0.6 s intervals, the R-peak detections set from Table 6 includes seven intervals from 0.53 s to 0.66 s. Thus, instead of sorting the ECG intervals into exact timing bins, the processor may sort the ECG intervals from Table 6 into ranged timing bins. As such, each of the histogram bins may encompass a range of ECG intervals corresponding to an estimated heart rate or a heart rate range.
  • configuring the histogram bins to each cover the same-sized range causes the histogram bins to have different resolutions. This is because for lower heart rates, a given difference in ECG interval lengths translates to smaller differences in the resulting heart rate than for higher heart rates, where the same given difference in ECG interval lengths translates to larger differences in the resulting heart rates. For instance, given ECG intervals 0.80 s, 0.82 s, and 0.84 s, the resulting heart rates are 75 bpm, 73 bpm, and 71 bpm. However, for ECG intervals 0.40 s, 0.42 s, and 0.44 s, the resulting heart rates are 150 bpm, 142 bpm, and 136 bpm.
  • the processor may configure the histogram bins to have a consistent resolution across the bins rather than having the same range across the bins. By setting the histogram bins to have a consistent resolution, the bins may instead cover fixed differences in frequency rather than interval size.
  • the processor may use histogram bins that are a predetermined resolution and spanning from a predetermined lower heart rate to a predetermined higher heart rate. For example, the processor may use histogram bins that have a resolution of 5 bpm, spanning from 40 bpm to 150 bpm. Table 7 below includes a list of the consequent 22 bins. For each of the histogram bins, Table 7 also includes the high end heart rate and the low end heart rate that the bin spans, as well as the center heart rate for the bin. In addition, Table 7 includes the range of ECG intervals that would be sorted into each bin.
  • ECG intervals greater than or equal to 0.4000 s and less than 0.4138 s may be sorted into bin 1, which corresponds to a heart rate of 147.5 bpm.
  • ECG intervals greater than 0.4000 s and less than or equal to 0.4138 s may be sorted into bin 1.
  • the histogram bins may be configured differently, such as by having different resolutions and/or lower/higher heart rate spans.
  • the histogram bins may have a 2-bpm resolution, a 3-bpm resolution, a 4-bpm resolution, etc.
  • the histogram bins may span from 20 bpm, 25 bpm, 30 bpm etc.
  • the histogram bins may also include a guard bin (e.g., a bin 0) into which, for example, the processor may sort ECG intervals less than 0.4000 s and greater than 1.5000 s.
  • a guard bin e.g., a bin 0
  • the processor may thus sort the example ECG intervals from Table 6 using the example histogram bins from Table 7 (e.g., performing step 1404 of FIG. 14), which produces the example histogram 1700 shown in FIG. 17.
  • the bin with the largest magnitude 1702 corresponds to the heart rate 102.5 bpm.
  • the processor may determine that the patient’s heart rate is approximately 102.5 bpm (e.g., performing steps 1406 and 1408 of FIG. 14). This result correlates well to the patient having a heart rate of 100 bpm with some noise.
  • the estimate of 102.5 bpm is within the 5-bpm resolution of the histogram bin, meaning that the processor identifying the 102.5 bpm bin is the processor correctly selecting the closest histogram bin to the actual heart rate.
  • the histogram bins with the next largest magnitudes 1704 and 1706 have almost as high of ECG interval counts as the largest bin 1702. Small changes in R-peak detections that add a few counts to either bin 1704 or 1706 could result in an incorrect heart rate determination. Additionally, in various examples, incorrect bins with high counts could be caused by harmonics in the ECG intervals. For example, the high count for bin 1704 could be caused by ECG intervals around 1.20 s, which as shown above with reference to histogram 1600 of FIG. 16 could be caused by harmonics in the patient’s ECG signals.
  • the processor can further use complex histograms as part of sorting the ECG intervals into histogram bins.
  • the processor does not store the integer count of the ECG intervals falling into each histogram bin (e.g., except for the guard bin, where the regular count is stored). Instead, the processor accumulates a complex value for each occurrence of an ECG interval that captures the occurrence and the phase of the occurrence relative to the center frequency of the histogram bin.
  • the processor can represent each occurrence as a complex exponential as follows, where T represents the time of detection and ftm represents the center frequency of the histogram bin in radians:
  • FIG. 18 shows an example of using the complex histogram method.
  • the peak with the greatest magnitude 1802 is still at heart rate 102.5 bpm.
  • the bins formerly having the next highest counts 1804 and 1806, at 52.5 bpm and 47.5 bpm. have been significantly reduced as shown.
  • the wearable cardiac defibrillator may calculate the patient’s heart rate using histograms as described herein. In implementations, the wearable cardiac defibrillator may calculate the patient’s heart rate using a preliminary heart rate determined using histograms as described herein. For example, the wearable cardiac defibrillator may calculate a preliminary heart rate using the process of sorting ECG intervals into histograms as described above. The wearable cardiac defibrillator may then use the preliminary heart rate as an input to an algorithm that outputs a finalized heart rate for the patient.
  • the wearable cardiac defibrillator may improve the detection of whether the patient is experiencing an arrhythmia condition by evaluating the heart rates determined from different ECG sensing channels or leads. Pairs of sensing electrodes may form the ECG sensing channels or leads.
  • the wearable cardiac defibrillator 200 may include a front-back ECG lead using the sensing electrodes 204a and 204c and a side-to-side ECG lead using the electrodes 204b and 204d.
  • the wearable cardiac defibrillator may use one of the ECG leads as a preferred stable heart rate source, with the wearable cardiac defibrillator defaulting to the heart rate determined from the ECG signals generated from the preferred ECG lead.
  • the wearable cardiac defibrillator may sense the electrical cardiac activity of the patient using the sensing leads of the wearable cardiac defibrillator and determine ongoing heart rates of the patient using the ECG signals generated from the preferred sensing lead.
  • the preferred ECG lead may be the side-to-side ECG lead in the example of the wearable cardiac defibrillator 200.
  • the preferred ECG lead may be user-selected, such as by a technician or a physician.
  • the wearable cardiac defibrillator 200 may therefore default to the side-to-side ECG lead in determining the heart rate for the patient. However, in implementations, the wearable cardiac defibrillator 200 may periodically compare the heart rates determined from the ECG signals generated from each ECG lead to identify which lead is likely producing the more accurate heart rate. The wearable cardiac defibrillator 200 may then confirm the preferred ECG lead or switch the preferred ECG lead depending on the results of the comparison.
  • FIG. 19 illustrates a sample process flow for determining ECG lead preference.
  • the sample process 1900 shown in FIG. 19 can be implemented by one or more processors of a wearable defibrillator, such as a processor of the cardiac controller 214 of the wearable cardiac defibrillator 200. Other embodiments of wearable cardiac defibrillators, including the other embodiments described herein, may similarly implement the sample process 1900.
  • each sensing lead may be composed of a pair of ECG sensing electrodes, such as pairs of ECG sensing electrodes 204.
  • the wearable cardiac defibrillator may have at least two leads (e.g., front-back and side-to-side leads, as described above). In implementations, the wearable cardiac defibrillator may have more than two leads. For example, the wearable cardiac defibrillator may include six ECG sensing electrodes forming three or more ECG sensing leads.
  • the processor generates ECG signals from the sensing leads at step 1904.
  • the processor may filter the ECG signals such that the processor produces raw and filtered ECG signals (e.g., as discussed with respect to step 902 of FIG. 8).
  • the processor determines ongoing heart rates of the patient using ECG signals generated from a preferred sensing lead at step 1906.
  • the processor may determine the heart rate of the patient by detecting R peaks using the processes described above with respect to FIGS. 8-13 and using the R-R intervals to calculate the patient’s heart rate.
  • the processor may calculate the patient’s heart rate by sorting ECG intervals into histogram bins as described above with respect to FIGS. 14-18.
  • the processor may then use the ongoing heart rates calculated from the preferred sensing lead to determine whether the patient is experiencing an arrhythmia condition, as described above with reference to FIGS. 3-7.
  • the ongoing heart rates may include the initial heart rate and the second heart rate discussed above.
  • the processor also determines at least one test heart rate for each of the sensing leads (e.g., using the ECG signals generated via each respective lead) at step 1908. For instance, the processor may continuously determine heart rates for each of the sensing leads at step 1908, with a running sample of the heart rates serving as test heart rates for each sensing lead. The processor may accordingly perform sample process 1900 on a continuous basis. As another example, the processor may continuously determine heart rates for the preferred sensing lead, with a sample or subset of the determined heart rates serving as test heart rate(s) for the preferred sensing lead. In this way, the processor may perform step 1908 as part of step 1906, as shown in FIG. 19.
  • the processor may also periodically determine one or more heart rates for the other, non-preferred sensing lead(s) when performing step 1908.
  • the periodically determined one or more heart rates for the non-preferred sensing lead(s) may serve as the remaining test heart rate(s).
  • the processor may determine test heart rates for the sensing leads (and thus perform the sample process 1900) once every 60 minutes, once every 90 minutes, once every' 120 minutes, once every 2 hours, once every' 4 hours, once every 6 hours, once every’ 12 hours, once every’ 24 hours, and/or so on.
  • the processor detects whether the test heart rates for each of the plurality of sensing leads agree with each other at step 1910. For example, the processor may determine whether the test heart rates for each of the sensing leads are within a certain predetermined amount from each other, such as within 5 bpm, 10 bpm, 15 bpm, 20 bpm, etc. from each other. As another example, the processor may determine whether the test heart rates for each of the sensing leads are within a predetermined proportion or percentage from each other. To illustrate, the processor may detect whether the test heart rates for each of the sensing leads are within 5%, 10%, 15% 20%, etc. from each other. The processor is further configured to determine whether to maintain the preferred sensing lead based on whether the test heart rates agree with each other.
  • the processor maintains the preferred sensing lead at step 1922.
  • the processor then returns to sensing electrical cardiac activity' using the sensing leads at step 1902.
  • the processor maintains determining the ongoing heart rates of the patient using the ECG signals generated via the preferred sensing lead at step 1906.
  • the processor may determine a representative test heart rate for each sensing lead and compare the representative heart rates. For instance, the processor may determine a mean or median test heart rate for each sensing lead, which the processor then uses to determine whether the test heart rates agree.
  • the processor determines whether noise is present in one or more of the sensing leads at step 1912. For example, the processor may run a noise analysis on the ECG signals generated from each sensing lead to determine whether any of the ECG signals includes a section of high-frequency identified R peaks (e.g., whether the ECG signal has a high guard bin count, as determined using sample process 1400). Such high-frequency peaks may be indicative of noise in the corresponding ECG signal. As another example, the processor may determine whether each ECG signal has an amplitude above a predetermined threshold amplitude level.
  • an ECG signal has an amplitude below the predetermined threshold amplitude level, this may be an indication that the corresponding sensing lead has poor signal quality' (e.g., the associated ECG sensing electrodes have poor skin contact).
  • the processor determines that noise is present in one or more of the sensing leads, the processor maintains the preferred sensing lead at step 1922. Otherwise, if the processor determines that noise is not present in any of the sensing leads, the processor moves to step 1914. In implementations, the processor may not perform step 1912 and instead move directly to step 1914 if the test heart rates do not agree at step 1910.
  • the processor determines whether certain conditions are met for switching the preferred sensing lead to another of the ECG sensing leads. As an illustration, as shown in FIG. 19, the processor may determine whether the test heart rate(s) for the preferred sensing lead is/are a predetermined amount above the test heart rate(s) for each non-preferred sensing lead at step 1914.
  • the predetermined amount may be, for instance, about X times more than the test heart rate(s) for each non-preferred sensing lead.
  • the processor is configured to switch the preferred sensing lead at step 1924.
  • the processor may switch the preferred sensing lead to the other of the sensing leads, with the preferred sensing lead becoming the non-preferred sensing lead.
  • the processor may analyze the currently non-preferred sensing leads to identify the best candidate to become the new preferred sensing lead.
  • the processor may switch the preferred sensing lead to the non-preferred sensing lead with the best signal quality (e.g., the highest amplitude, the least amount of noise, etc.).
  • the processor After changing the preferred sensing lead, the processor returns to sensing electrical cardiac activity at step 1902. Accordingly, the new preferred sensing lead determines the ongoing heart rates for the patient using the ECG signal generated via the new preferred (previously non-preferred) sensing lead at step 1906.
  • the processor determines whether the test heart rate(s) from the preferred sensing lead transgress(es) the VT threshold at step 1916.
  • the VT threshold may be any of the example VT thresholds discussed above (e.g., in Tables 1-4). If the test heart rate(s) from the preferred lead do(es) not transgress the VT threshold, the processor maintains the preferred sensing lead at step 1922.
  • the processor determines whether the test heart rate(s) from the non-preferred sensing lead(s) transgress(es) the VT threshold at step 1918. If the processor determines that the test heart rate(s) from the non-preferred sensing lead(s) do(es) transgress the VT threshold, the processor maintains the preferred sensing lead at step 1922 (e.g., as both sets of test heart rates transgressing the VT threshold may be indicative that the patient is experiencing an arrhythmia condition).
  • the processor determines whether the heart rate calculated using a FFT detector (e.g., as described above with reference to FIG. 4) transgresses the VT threshold. If the heart rate from the FFT detector does transgress the VT threshold, the processor maintains the preferred sensing lead at step 1922 (e.g., due to the FFT detector heart rate indicating that the patient may be experiencing an arrhythmia condition).
  • a FFT detector e.g., as described above with reference to FIG. 4
  • the processor switches the preferred sensing lead to a non-preferred sensing lead at step 1924.
  • the processor may not perform step 1920 and instead switch the preferred sensing lead based just on the test heart rates generated from the sensing leads.
  • the processor may establish the new lead as the preferred lead.
  • the processor may then repeat the process 1900 of determining whether to maintain the current preferred lead or to switch the preferred lead based on, for example, a window.
  • a window may be a window of 10 seconds. 15 seconds, 20 seconds, 25 seconds, 30 seconds, 40 seconds. 50 seconds, 60 seconds, 70 seconds, 80 seconds. 90 seconds, and/or the like.
  • the processor may again evaluate the current preferred lead.
  • the window may be an adjustable window. For instance, a caregiver or technician may set the window (e.g., during an initial fitting and baselining for a patient prescribed the wearable cardiac defibrillator).
  • the window may be a continuous window such that the processor continually evaluates the preferred lead.
  • the wearable cardiac defibrillator may combine implementations and embodiments as described herein.
  • the wearable cardiac defibrillator may continuously or periodically perform the process of switching the preferred sensing lead.
  • the wearable cardiac defibrillator may detect R peaks in the ECG signals generated from the preferred sensing lead by analyzing local maxima in the ECG signals.
  • the wearable cardiac defibrillator may establish the heart rate for the patient by calculating ECG intervals using the R peak detections, sorting the ECG intervals into histogram bins, and determining the heart rate from the histogram bin with the greatest magnitude.
  • the wearable cardiac defibrillator may then use the patient’s heart rates to perform an initial determination that the patient is experiencing a cardiac arrhythmia condition, determine one or more subsequent heart rates for the patient, calculate one or more heart rate scores for the patient, and decide whether to initiate a treatment sequence based on the one or more calculated heart rate scores.
  • FIG. 20 illustrates a sample component-level view of a cardiac controller 2000.
  • the cardiac controller 2000 is an example of the cardiac controller 214 shown in FIG. 1 and described above.
  • the cardiac controller 2000 may include a housing 2002 configured to house a number of electronic components, including a sensor interface 2004, a data storage 2006, a network interface 2008, a user interface 2010, at least one battery 2012 (e.g., positioned within a battery chamber configured for such a purpose), a cardiac event detector 2014, an alarm manager 2016, a therapy delivery circuit 2018, and at least one processor 2020.
  • the processor 2020 includes one or more processors (or one or more processor cores) that are each configured to perform a series of instructions that result in the manipulation of data and/or the control of the operation of the other components of the cardiac controller 2000.
  • the processor 2020 when executing a specific process (e.g., monitoring sensed electrical data of the patient 202), the processor 2020 can be configured to make specific logic-based determinations based on input data received.
  • the processor 2020 may be further configured to provide one or more outputs that can be used to control or otherwise inform subsequent processing to be carried out by the processor 2020 and/or other processors or circuitry to which the processor 2020 is communicably coupled.
  • the processor 2020 reacts to a specific input stimulus in a specific w ay and generates a corresponding output based on that input stimulus.
  • the processor 2020 can proceed through a sequence of logical transitions in which various internal register states and/or other bit cell states internal or external to the processor 2020 may be set to logic high or logic low.
  • the processor 2020 can be configured to execute a function where software is stored in a data store (e.g., the data storage 2006) coupled to the processor 2020, the software being configured to cause the processor 2020 to proceed through a sequence of various logic decisions that result in the function being executed.
  • a data store e.g., the data storage 2006
  • the various components that are described herein as being executable by the processor 2020 can be implemented in various forms of specialized hardware, software, or a combination thereof.
  • the processor 2020 can be a digital signal processor (DSP) such as a 24-bit DSP processor.
  • the processor 2020 can be a multi-core processor, e.g., having two or more processing cores.
  • the processor 2020 can be an Advanced RISC Machine (ARM) processor, such as a 32-bit ARM processor.
  • the processor 2020 can execute an embedded operating system and further execute services provided by the operating system, where these services can be used for file system manipulation, display and audio generation, basic networking, firewalling, data encryption, communications, and/or the like.
  • the data storage 2006 can include one or more of a non -transitory computer-readable medium or media, such as flash memory, solid state memory, magnetic memory', optical memory, cache memory, combinations thereof, and others.
  • the data storage 2006 can be configured to store executable instructions and data used for operation of the cardiac controller 2000.
  • the data storage 2006 can include sequences of executable instructions that, when executed, are configured to cause the processor 2020 to perform one or more functions.
  • the data storage 2006 can be configured to store information such as digitized ECG signals of the patient 202.
  • the network interface 2008 can facilitate the communication of information between the cardiac controller 2000 and one or more devices or entities over a communications network.
  • the network interface 2008 can be configured to communicate with a remote server or other similar computing device.
  • the wearable cardiac defibrillator 200 may transmit, for example, ECG signals, other physiological signals, indications of abnormal cardiac events, etc., to the remote server.
  • the network interface 2008 can include communications circuitry for transmitting data in accordance with a Bluetooth® wireless standard for exchanging such data over short distances to an intermediary device(s) (e.g., a base station, “hotspof’ device, smartphone, tablet, portable computing device, and/or other device in proximity with the wearable cardiac defibrillator 200).
  • the intermediary device(s) may in turn communicate the data to the remote server over a broadband cellular network communications link.
  • the communications link may implement broadband cellular technology (e.g., 2.5G, 2.75G, 3G. 4G, 5G cellular standards) and/or Long-Term Evolution (LTE) technology or GSM/EDGE and UMTS/HSPA technologies for high-speed wireless communication.
  • broadband cellular technology e.g., 2.5G, 2.75G, 3G. 4G, 5G cellular standards
  • LTE Long-Term Evolution
  • the intermediary' device(s) may communicate with the remote server over a Wi-Fi communications link based on the IEEE 802. 11 standard.
  • the network interface 2008 may be configured to instead communicate directly with the remote server without the use of intermediary device(s). In such implementations, the network interface 2008 may use any of the communications links and/or protocols provided above to communicate directly with the remote server.
  • the sensor interface 2004 can include physiological signal circuitry that is coupled to one or more sensors 2022 configured to be externally applied to the patient 202. As shown, the sensors may be coupled to the cardiac controller 2000 via a wired or wireless connection.
  • the externally applied sensors 2022 may include the ECG electrodes 204 configured to sense one or more electrical signals indicative of ECG activity from the skin surface of the patient 202, as well as one or more non-ECG sensors such as a cardiovibration sensor 2024 and a tissue fluid monitor 2026.
  • externally applied sensors 2022 may include a respiration sensor, a bioacoustics sensor, a blood pressure sensor, a temperature sensor, a pressure sensor, a humidity sensor, a P-wave sensor (e.g., a sensor configured to monitor and isolate P-waves within an ECG waveform), an oxygen saturation sensor (e.g., implemented through photoplethysmography, such as through light sources and light sensors configured to transmit light into the patient's body and receive transmitted and/or reflected light containing information about the patient’s oxygen saturation), and so on.
  • a respiration sensor e.g., a bioacoustics sensor, a blood pressure sensor, a temperature sensor, a pressure sensor, a humidity sensor, a P-wave sensor (e.g., a sensor configured to monitor and isolate P-waves within an ECG waveform), an oxygen saturation sensor (e.g., implemented through photoplethysmography, such as through light sources and light sensors configured to transmit light into the patient's body and receive transmitted and/
  • the one or more cardiovibration sensors 2024 can be configured to detect cardiac or pulmonary' vibration information.
  • the one or more cardiovibration sensors 2024 can transmit information descriptive of the cardiovibrations (and other types of sensed vibrations) to the sensor interface 2004 for subsequent analysis.
  • the one or more cardiovibration sensors 2024 can detect the patient’s heart valve vibration information (e.g., from opening and closing during cardiac cycles).
  • the one or more cardiovibration sensors 2024 can be configured to detect cardiovibrational signal values including one or more of SI, S2. S3, and S4 cardiovibrational biomarkers.
  • certain heart vibration metrics may be calculated (e.g., at the wearable cardiac defibrillator 200 and/or at a remote server). These heart vibration metrics may include one or more of electromechanical activation time (EMAT), average EMAT, percentage of EMAT (% EMAT), systolic dysfunction index (SDI). or left ventricular systolic time (LVST).
  • EMAT electromechanical activation time
  • % EMAT percentage of EMAT
  • SDI systolic dysfunction index
  • LVST left ventricular systolic time
  • the one or more cardiovibration sensors 2024 can also be configured to detect heart wall motion, for instance, by placement of the sensor in the region of the apical beat.
  • the one or more cardiovibration sensors 2024 can include a vibrational sensor configured to detect vibrations from the patient’s cardiac and pulmonary’ system and provide an output signal responsive to the detected vibrations of a targeted organ.
  • the one or more cardiovibration sensors 2024 may be configured to detect vibrations generated in the trachea or lungs due to the flow of air during breathing.
  • additional physiological information can be determined from pulmonary-vibrational signals such as, for example, lung vibration characteristics based on sounds produced within the lungs (e.g., stridor, crackle, etc.).
  • the one or more cardiovibration sensors 2024 can include a multi-channel accelerometer, for example, a three-channel accelerometer configured to sense movement in each of three orthogonal axes such that patient movement/body position can be detected and correlated to detected cardiovibration information.
  • the tissue fluid monitor 2026 may include a radiofrequency (RF) sensor configured to take bio-impedance measurements of the patient’s thorax.
  • sensor interface 2004 may use the bio-impedance measurements to determine a thoracic fluid level in the patient 202.
  • An example embodiment of the tissue fluid monitor 2026 includes at least one RF antenna, such as a transmitting antenna and a receiving antenna, or a single antenna configured to transmit and receive RF waves.
  • the tissue fluid monitor 2026 also includes RF circuitry configured to transmit a low-power signal in an ultra-high frequency band (e.g., 0.1 GHz to 5.0 GHz, 0.5 GHz to 2.1 GHz) at a predetermined rate (e.g., every 10 ms, every 20 ms, every 30 ms, ever ⁇ ' 40 ms, every 50 ms, etc.).
  • the tissue fluid monitor 2026 receives RF-based biosignals indicative of the thoracic fluid level in the patient 202 in the form of RF waves transmitted through the patient 202, scattered by the patient 202, and/or reflected from the patient 202.
  • the tissue fluid monitor 2026 may detect transmitted, scattered, and/or reflected RF waves for a predetermined amount of time (e.g., about 30 seconds, about 1 minute, about 2 minutes, about 3 minutes, about 5 minutes, about 10 minutes, etc.).
  • a predetermined amount of time e.g., about 30 seconds, about 1 minute, about 2 minutes, about 3 minutes, about 5 minutes, about 10 minutes, etc.
  • the wearable cardiac defibrillator 200 is configured to gate when RF measurements are taken and/or discard certain RF measurements based on the patient’s state when the RF measurements were taken.
  • the sensor interface 2004 may determine whether the patient 202 showed movement above a predetermined threshold before tissue fluid monitor 2026 started the RF measurements process and/or while the RF measurements were taking place. If RF measurements were taken during or immediately after movement above the predetermined threshold, the sensor interface 2004 may discard those RF measurements.
  • at least some of the analysis and/or gating of RF measurements may be performed by a remote server.
  • the cardiac controller 2000 may transmit the RF measurements to the remote server via the network interface 2008 and receive back from the remote server a determined thoracic fluid level.
  • the sensor interface 2004 may be connected to one or more motion sensors (e.g.. one or more accelerometers, gyroscopes, magnetometers, ballistocardiographs, etc.) as part of the externally applied sensors 2022.
  • the cardiac controller 2000 may include a motion sensor interface, either implemented separately or as part of the sensor interface 2004. For instance, as shown in FIG.
  • the cardiac controller 2000 may include a motion sensor interface 2028 operatively coupled to one or more motion detectors 2030 configured to generate motion data, for example, indicative of physical activity performed by the patient 202 and/or physiological information internal to the patient 202.
  • a motion detector may include a 1-axis channel accelerometer, 2-axis channel accelerometer, 3-axis channel accelerometer, multi-axis channel accelerometer, gy roscope, magnetometer, ballistocardiograph, and the like.
  • the motion data may include accelerometer counts indicative of physical activity performed by the patient 202, accelerometer counts indicative of respiration rate of the patient 202, accelerometer counts indicative of posture information for the patient 202, accelerometer counts indicative of cardiovibrational information for the patient 202, and/or the like.
  • the motion sensor interface 2028 is configured to receive one or more outputs from the motion sensors 2030.
  • the motion sensor interface 2028 can be further configured to condition the output signals by, for example, converting analog signals to digital signals (if using an analog motion sensor), filtering the output signals, and/or combining the output signals into a combined directional signal (e.g.. combining each x-axis signal into a composite x-axis signal, combining each y-axis signal into a composite y-axis signal, and combining each z-axis signal into a composite z-axis signal).
  • the motion sensor interface 2028 can be configured to filter the signals using a high-pass or band-pass filter to isolate the acceleration of the patient 202 due to movement from the component of the acceleration due to gravity. Additionally, the motion sensor interface 2028 can configure the outputs from the motion sensor(s) 2030 for further processing. For example, the motion sensor interface 2028 can be configured to arrange the output of an individual motion sensor 2030 as a vector expressing acceleration components of the x-axis, the y-axis, and the z-axis of the motion sensor 2030. The motion sensor interface 2028 can thus be operably coupled to the processor 2020 and configured to transfer the output and/or processed motion signals from the motion sensors 2030 to the processor 2020 for further processing and analysis.
  • the one or more motion sensors 2030 can be integrated into one or more components of the wearable cardiac defibrillator 200, either within the cardiac controller 2000 or external to the cardiac controller 2000, as shown in FIG. 20.
  • the one or more motion detectors 2030 may be located in or near the ECG electrodes 204.
  • the one or more motion detectors 2030 may be located elsewhere on the wearable cardiac defibrillator 200.
  • a motion detector 2030 may be integrated into the cardiac controller 2000 (e.g., such that the one or more motion detectors 2030 would be located within the housing 2002 of the cardiac controller 2000, as shown in FIG. 20).
  • a motion detector 2030 may be integrated into another component of the wearable cardiac defibrillator 200. such as a therapy electrode 206. the connection pod 210, and/or the like. In some implementations, a motion detector 2030 can be integrated into an adhesive ECG sensing and/or therapy electrode patch.
  • the sensor interface 2004 and/or the processor 2020 may be configured to provide digitized ECG signals of the patient 202 based on electrical signals sensed by the ECG electrodes 204. In this sense, the sensor interface 2004 and/or the processor 2020 may be considered an ECG digitizing circuit. The digitized ECG signals of the patient 202 may be stored in the data storage 2006. In implementations, the connection pod 210 and/or the sensing electrodes 204 themselves may instead perform the digitizing of the ECG signals, as discussed above.
  • the sensor interface 2004 and the motion sensor interface 2028 can be coupled to any one or combination of external sensors to receive patient data indicative of patient parameters.
  • the data can be directed by the processor 2020 to an appropriate component within the cardiac controller 2000.
  • ECG signals collected by the sensing electrodes 204 may arrive at the sensor interface 2004, and the sensor interface 2004 may transmit the ECG signals to the processor 2020, which, in turn, relays the patient's ECG data to the cardiac event detector 2014.
  • the sensor data can also be stored in the data storage 2006 and/or transmitted to a remote server via the network interface 2008.
  • the cardiac event detector 2014 can be configured to monitor the patient's ECG signal for an occurrence of a cardiac event such as an arrhythmia or other similar event.
  • the cardiac event detector 2014 can be configured to operate in concert with the processor 2020 to execute one or more methods that process received ECG signals from, for example, the sensing electrodes 204 and determine the likelihood that the patient 202 is experiencing a cardiac event, such as a treatable arrhythmia.
  • the cardiac event detector 2014 can be implemented using hardware or a combination of hardware and software. For instance, in some examples, the cardiac event detector 2014 can be implemented as a software component that is stored within the data storage 2006 and executed by the processor 2020.
  • the instructions included in the cardiac event detector 2014 can cause the processor 2020 to perform one or more methods for analyzing a received ECG signal to determine whether an adverse cardiac event is occurring, such as a treatable arrhythmia.
  • the cardiac event detector 2014 can be an application-specific integrated circuit (ASIC) that is coupled to the processor 2020 and configured to monitor ECG signals for adverse cardiac event occurrences.
  • ASIC application-specific integrated circuit
  • the cardiac event detector 2014 may also be configured to record an ECG segment when the cardiac event detector 2014 determines that the patient is experiencing a cardiac event.
  • the ECG segment may be a segment that starts when the cardiac event detector 2014 identifies a cardiac event and ends after the patient receives a treatment shock or after the cardiac event detector 2014 drops the cardiac event detection.
  • the ECG segment may be a segment of predetermined length that starts at or before the cardiac event detector 2014 identifies the cardiac event.
  • the cardiac event detector 2014 may be configured to store the ECG segment in the data storage 2006.
  • the cardiac controller 2000 may then transmit the ECG segment, for example, to a remote server for review (e.g., by the patient’s caregiver and/or a technician).
  • the user interface 2010 may include one or more physical interface devices, such as input devices, output devices, and combination input/output devices, as well as a software stack configured to drive operation of the devices. These user interface elements may render visual, audio, and/or tactile content. Thus, the user interface 2010 may receive inputs and/or provide outputs, thereby enabling a user to interact with the cardiac controller 2000.
  • the cardiac controller 2000 can also include at least one battery 2012 configured to provide power to one or more components integrated in the cardiac controller 2000.
  • the battery 2012 can include a rechargeable multi-cell battery pack.
  • the battery 2012 can include three or more cells (e.g., 2200 mA lithium-ion cells) that provide electrical power to the other device components within the cardiac controller 2000.
  • the battery 2012 can provide its power output in a range of between a 20 mA to 1000 mA (e.g., 40 mA) output and can support 24 hours, 48 hours, 72 hours, or more of runtime between charges.
  • the battery capacity, runtime, and type can be changed to best fit the specific application of the cardiac controller 2000.
  • the wearable cardiac defibrillator 200 is configured to provide therapeutic shocks to the patient 202 upon detecting that the patient 202 is experiencing a treatable arrhythmia.
  • the therapy delivery circuit 2018 can be coupled to the therapy electrodes 206 that are configured to provide therapy to the patient 202.
  • the therapy delivery 7 circuit 2018 can include, or be operably connected to, circuitry components that are configured to generate and provide an electrical therapeutic shock.
  • the circuitry components can include, for example, resistors, capacitors, relays and/or switches, electrical bridges such as an H-bridge (e.g., including a plurality 7 of insulated gate bipolar transistors or IGBTs), voltage and/or current measuring components, and other similar circuitry 7 components.
  • the circuitry 7 components are arranged and connected such that the circuitry components work in concert with the therapy delivery circuit 2018 and under the control of one or more processors (e.g., processor 2020) to provide various therapeutic pulses. Examples of therapeutic pulses include one or more of pacing, defibrillation, or cardioversion therapeutic pulses.
  • pacing pulses can be used to treat cardiac arrhythmias such as bradycardia (e.g., less than 30 beats per minute) and tachycardia (e.g., more than 150 beats per minute) using, for example, fixed rate pacing, demand pacing, anti-tachycardia pacing, and the like.
  • defibrillation or cardioversion pulses can be used to treat ventricular tachycardia and/or ventricular fibrillation.
  • the therapy delivery circuit 2018 includes a first high-voltage circuit connecting a first pair of the therapy electrodes 206 (e.g., the therapy electrode 206a and one of the therapy electrodes 206b) and a second high-voltage circuit connecting a second pair of the therapy electrodes 206 (e.g., the therapy electrode 206a and the other of the therapy electrodes 206b).
  • the therapy delivery 7 circuit 2018 may deliver a first biphasic therapeutic pulse via the first high-voltage circuit and a second biphasic therapeutic pulse via the second high-voltage circuit.
  • the second high-voltage circuit is configured to be electrically isolated from the first high-voltage circuit.
  • the therapy delivery 7 circuit 2018 includes a capacitor configured to be selectively connected to the first high-voltage circuit and/or the second high-voltage circuit.
  • the first high- voltage circuit may be powered by the capacitor when the capacitor is selectively connected to the first high-voltage circuit
  • the second high-voltage circuit may 7 be powered by the capacitor when the capacitor is selectively connected to the second high-voltage circuit.
  • the therapy delivery circuit 2018 includes a first capacitor electrically connected to the first high-voltage circuit and a second capacitor electrically connected to the second high-voltage circuit.
  • the capacitors can include a parallel-connected capacitor bank consisting of a plurality of capacitors (e.g., two. three, four, or more capacitors).
  • the capacitors can include a single film or electrolytic capacitor as a series connected device including a bank of the same capacitors. These capacitors can be switched into a series connection during discharge for a defibrillation pulse. For example, four capacitors of approximately 140 pF or larger, or four capacitors of approximately 650 pF, can be used.
  • the capacitors can have a 1600 VDC or higher rating for a single capacitor, or a surge rating between approximately 350 to 500 VDC for paralleled capacitors, and can be charged in approximately 15 to 30 seconds from a battery pack.
  • each defibrillation pulse can deliver between 60 J to 180 J of energy.
  • the defibrillating pulse can be a biphasic truncated exponential waveform, whereby the signal can switch between a positive and a negative portion (e.g., charge directions).
  • This type of waveform can be effective at defibrillating patients at lower energy levels when compared to other types of defibrillation pulses (e.g., such as monophasic pulses).
  • an amplitude and a width of the two phases of the energy waveform can be automatically adjusted to deliver a precise energy amount (e.g., 150 J) regardless of the patient’s body impedance.
  • the therapy delivery circuit 2018 can be configured to perform the switching and pulse delivery operations, e.g., under control of the processor 2020. As the energy is delivered to the patient 202, the amount of energy being delivered can be tracked. For example, the amount of energy can be kept to a predetermined constant value even as the pulse waveform is dynamically controlled based on factors such as the patient’s body impedance while the pulse is being delivered. In certain examples, the therapy delivery' circuit 2018 can be configured to deliver a set of cardioversion pulses to correct, for example, an improperly beating heart. When compared to defibrillation as described above, cardioversion ty pically includes a less powerful shock that is delivered at a certain frequency to mimic a heart’s normal rhythm.
  • the alarm manager 2016 can be implemented using hardware or a combination of hardware and software.
  • the alarm manager 2016 can be implemented as a software component that is stored within the data storage 2006 and executed by the processor 2020.
  • the instructions included in the alarm manager 2016 can cause the processor 2020 to configure alarm profiles and notify intended recipients using the alarm profiles.
  • the alarm manager 2016 can be an application-specific integrated circuit (ASIC) that is coupled to the processor 2020 and configured to manage alarm profiles and notify intended recipients using alarms specified within the alarm profiles.
  • ASIC application-specific integrated circuit
  • the alarm manager 2016 can be configured to manage alarm profiles and notify one or more intended recipients of events, where an alarm profile includes a given event and the intended recipient(s) who may have in interest in the given event.
  • the intended recipient(s) can include external entities, such as users (e.g.. patients, physicians and other caregivers, a patient's loved one. monitoring personnel), as well as computer systems (e.g., monitoring systems or emergency response systems, such as a remote server in communication with the wearable cardiac defibrillator 200).
  • the processor 2020 is configured to deliver a cardioversion/defibrillation shock to the patient 202 via the therapy electrodes 206.
  • the alarm manager 2016 may issue an alarm (e.g., via the user interface 2010, the patient interface pod 212. and/or the connection pod 210) that the patient 202 is about to experience a defibrillating shock.
  • the alarm may include auditory, tactile, and/or other types of alerts.
  • the alerts may increase in intensify over time, such as increasing in pitch, increasing in volume, increasing in frequency, switching from a tactile alert to an auditory alert, and so on.
  • the alerts may inform the patient 202 that the patient 202 can abort the delivery of the defibrillating shock by interacting with the user interface 2010.
  • the patient 202 may be able to press a user response button or user response buttons on the user interface 2010, after which the alarm manager 2016 will cease issuing an alert and the cardiac controller 2000 will no longer prepare to deliver or delay delivering the therapeutic shock.
  • the patient 202 may additionally or alternatively be able to interact with one or more user interface buttons disposed elsewhere on the wearable cardiac defibrillator 200, such as on patient interface pod 212 (e.g., press a user response button or user response buttons on the patient interface pod 212), to abort or delay the therapeutic shock.
  • FIG. 21 illustrates another embodiment of a wearable cardiac defibrillator that can use the systems, methods, and techniques discussed above (e.g.. implementing improved heart rate detection for arrhythmia warnings and treatment). More specifically, FIG. 21 shows a hospital wearable defibrillator 2100 that is external, ambulatory, and wearable by a patient 2101.
  • hospital wearable defibrillator 2100 can be configured to also provide pacing therapy, e.g., to treat bradycardia, tachycardia, and asystole conditions.
  • the patient 2101 may use the hospital wearable defibrillator 2100 in hospital settings (e.g., for cardiac protection after a cardiac event), or the patient 2101 may use the hospital wearable defibrillator in other settings where the patient 2101 is ambulatory (e.g., at the patient's home and workplace).
  • the hospital wearable defibrillator 2100 can include one or more sensing electrodes 2102a, 2102b, 2102c (e.g., collectively sensing electrodes 2102), therapy electrodes 2104a and 2104b (e.g., collectively therapy electrodes 2104), a cardiac controller 2106, and a signal processing unit 2108.
  • each of these components can function similarly to the like components of the wearable cardiac defibrillator 200 discussed above with reference to FIG. 1.
  • the electrodes 2102 and 2104 can include disposable adhesive electrodes.
  • the electrodes 2102 and 2104 can include sensing and therapy components disposed on separate sensing and therapy electrode adhesive patches.
  • both sensing and therapy components can be integrated and disposed on a same electrode adhesive patch that is then attached to the patient 2101.
  • the front therapy electrode 2104a attaches to the front of the patient’s torso to deliver pacing or defibrillation therapy.
  • the back therapy electrode 2104b attaches to the back of the patient’s torso.
  • at least three ECG adhesively attachable sensing electrodes 2102 can be attached at least above the patient’s chest near the right arm (e.g., electrode 2102a), above the patient’s chest near the left arm (e.g., electrode 2102b), and towards the bottom of the patient’s chest (e.g., electrode 2102c) in a manner prescribed by a trained professional.
  • the hospital wearable defibrillator 2100 may include additional adhesive therapy electrodes 2104. Additionally, or alternatively, the patches shown in FIG. 21 may include additional therapy electrode surfaces on them, such that at least two vectors may be formed between the therapy electrodes 2104 of the hospital wearable defibrillator 2100.
  • the cardiac controller 2106 may be configured to function similarly to the cardiac controller 214 of FIG. 1 and cardiac controller 2000 of FIG. 20. As shown in FIG. 21, the cardiac controller 2106 may include a user interface 2110 configured to communicate information with the patient 2101.
  • the patient 2101 being monitored by a hospital wearable defibrillator 2100 may be confined to a hospital bed or room for a significant amount of time (e.g., 75% or more of the patient’s stay in the hospital).
  • a user interface 2110 can be configured to interact with a user other than the patient 2101 (e.g., a technician, a clinician or other caregiver) for device-related functions such as initial device baselining, setting and adjusting patient parameters, and changing the device batteries.
  • a user other than the patient 2101 e.g., a technician, a clinician or other caregiver
  • device-related functions such as initial device baselining, setting and adjusting patient parameters, and changing the device batteries.
  • FIG. 22 illustrates another embodiment of a wearable cardiac defibrillator that can use the systems, methods, and techniques discussed above (e g., implementing improved heart rate detection for arrhythmia warnings and treatment).
  • the wearable cardiac defibrillator may be or may include an adhesive assembly 2200.
  • the adhesive assembly 2200 includes a contoured pad 2202 and a housing 2204 configured to form a watertight seal with the contoured pad 2202.
  • the housing 2204 is configured to house electronic components of the adhesive assembly 2200, such as electronic components forming a cardiac controller (e.g., similar to the cardiac controller embodiments discussed above).
  • the adhesive assembly 2200 includes a conductive adhesive layer 2206 configured to adhere the adhesive assembly 2200 to a skin surface 2208 of a patient.
  • the adhesive layer 2206 may include, for example, a water-vapor permeable conductive adhesive material, such as a material selected from the group consisting of an electro-spun polyurethane adhesive, a polymerized microemulsion pressure sensitive adhesive, an organic conductive polymer, an organic semi- conductive conductive polymer, an organic conductive compound, and a semi-organic conductive compound, and combinations thereof.
  • the adhesive assembly 2200 also includes at least one therapy electrode 2210 integrated with the contoured pad 2202.
  • the adhesive assembly 2200 may include a therapy electrode 2210 that forms a vector with another therapy electrode disposed on another adhesive assembly 2200 adhered to the patient’s body and/or with a separate therapy electrode adhered to the patient’s body.
  • the adhesive assembly 2200 may also include one or more sensing electrodes 2212 integrated with the contoured pad 2202 (e.g., sensing electrodes 2212a and 2212b).
  • the adhesive assembly 2200 may alternatively or additionally be in electronic communication with a separate sensing electrode, such as an adhesive sensing electrode adhered to the patient’s body. In examples, as shown in FIG.
  • the therapy electrode(s) 2210 and sensing electrode(s) 2212 may be formed within the contoured pad 2202 such that a skin-contacting surface of each component is coplanar with or protrudes from the patient-contacting face of the contoured pad 2202.
  • Examples of a wearable cardiac device including an adhesive assembly 2200 are described in U.S. Patent Application No. 16/585,344, entitled “Adhesively Coupled Wearable Medical Device,” filed on September 27, 2019. which is hereby incorporated by reference in its entirety.
  • FIG. 23 illustrates another example of a wearable cardiac defibrillator that can use the systems, methods, and techniques discussed above (e.g., implementing improved heart rate detection for arrhythmia warnings and treatment).
  • a wearable cardiac defibrillator may include a belted wearable defibrillator 2300 that is external, ambulatory’, and wearable by a patient 2301.
  • the belted wearable defibrillator 2300 may include a cardiac controller 2302 configured to be worn mounted on a belt 2304 around the patient’s torso.
  • the belted wearable defibrillator 2300 may be configured similarly to the hospital wearable defibrillator 2100 shown in FIG. 21.
  • the belted wearable defibrillator 2300 may instead include a cardiac controller 2302 integrated into the belt 2304.
  • the belt 2304 includes a number of modules housing the circuitry of the cardiac controller 2302 such that the patient 2301 does not need to wear a separate cardiac controller 2302.
  • the cardiac controller 2302 implemented either as a separate unit or as integrated into the belt 2304 may be configured to function similarly to the medical device controller embodiments described above.
  • the belted wearable defibrillator 2300 can include adhesive electrodes 2306a, 2306b, 2306c (e.g., collectively adhesive electrodes 2306) configured to be attached to the patient's skin.
  • the adhesive electrodes 2306 may be disposable adhesive electrodes in a wired connection 2308 with the cardiac controller 2302 (or, in implementations, with the belt 2304 including the circuitry' of the cardiac controller 2302).
  • at least some of the adhesive electrodes 2306 may be wirelessly connected to the cardiac controller 2302 (or, in implementations, with the belt 2304 including the circuitry of the cardiac controller 2302).
  • the adhesive electrodes 2306 may be configured to communicate via Bluetooth® with the cardiac controller 2302 (or the belt 2304).
  • at least some of the adhesive electrodes 2306 may include both sensing and therapy components integrated into the same electrode adhesive patch that is attached to the patient.
  • at least some of the adhesive electrodes 2306 may be a dedicated sensing electrode or a dedicated therapy electrode.
  • adhesive electrodes 2306a and 2306c may be dedicated therapy electrodes.
  • the belted wearable defibrillator 2300 may include additional adhesive electrodes 2306 with sensing and/or therapy components configured to form additional sensing and/or therapy electrode vectors.
  • inventive concepts may be embodied as one or more methods, of which an example has been provided.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

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Abstract

L'invention concerne un défibrillateur cardiaque portable ayant une capacité de détection de fréquence cardiaque améliorée pour la surveillance et le traitement des arythmies comprenant des électrodes capteurs configurées pour capter l'activité cardiaque électrique d'un patient, des électrodes de thérapie configurées pour administrer des chocs thérapeutiques au cœur du patient et un contrôleur cardiaque. Le contrôleur cardiaque est configuré pour effectuer une détermination initiale d'une condition d'arythmie sur la base d'une analyse d'arythmie de signaux ECG de patient, identifier et enregistrer une fréquence cardiaque initiale associée à la détermination initiale et générer un score de fréquence cardiaque initiale (FC)à l'aide de la fréquence cardiaque initiale. Le contrôleur cardiaque est également configuré pour déterminer une seconde fréquence cardiaque consécutive à la fréquence cardiaque initiale, générer un score de fréquence cardiaque de vérification d'arythmie à l'aide du score de FC initial et de la seconde fréquence cardiaque, comparer le score de FC de vérification d'arythmie à un seuil de score de FC prédéterminé et déterminer s'il faut initier une séquence de traitement pour le patient sur la base de la comparaison.
PCT/US2025/019128 2024-03-13 2025-03-10 Score de fréquence cardiaque pour défibrillateurs cardioverteurs portables Pending WO2025193585A1 (fr)

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US63/564,622 2024-03-13

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190275335A1 (en) * 2018-03-12 2019-09-12 Zoll Medical Corporation Verification of cardiac arrhythmia prior to therapeutic stimulation
WO2023192123A1 (fr) * 2022-03-28 2023-10-05 Zoll Medical Corporation Détection d'arythmie à distance et analyse de traitement dans des dispositifs cardiaques portables
WO2024006829A1 (fr) * 2022-06-30 2024-01-04 Zoll Medical Corporation Défibrillation à double vecteur séquentiel et multiple pour défibrillateurs automatiques portables

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190275335A1 (en) * 2018-03-12 2019-09-12 Zoll Medical Corporation Verification of cardiac arrhythmia prior to therapeutic stimulation
WO2023192123A1 (fr) * 2022-03-28 2023-10-05 Zoll Medical Corporation Détection d'arythmie à distance et analyse de traitement dans des dispositifs cardiaques portables
WO2024006829A1 (fr) * 2022-06-30 2024-01-04 Zoll Medical Corporation Défibrillation à double vecteur séquentiel et multiple pour défibrillateurs automatiques portables

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