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WO2024223298A1 - Système de capteur et procédé mis en œuvre par ordinateur pour évaluer une physiologie d'un patient post-infarctus - Google Patents

Système de capteur et procédé mis en œuvre par ordinateur pour évaluer une physiologie d'un patient post-infarctus Download PDF

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Publication number
WO2024223298A1
WO2024223298A1 PCT/EP2024/059711 EP2024059711W WO2024223298A1 WO 2024223298 A1 WO2024223298 A1 WO 2024223298A1 EP 2024059711 W EP2024059711 W EP 2024059711W WO 2024223298 A1 WO2024223298 A1 WO 2024223298A1
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WO
WIPO (PCT)
Prior art keywords
sensor device
wave
patient
leadless
sensor system
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/EP2024/059711
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English (en)
Inventor
Thomas Doerr
Thomas KRUCZYK
Sebastian Schulze-Luckow
Frank Becker
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.)
Biotronik SE and Co KG
Original Assignee
Biotronik SE and Co KG
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 Biotronik SE and Co KG filed Critical Biotronik SE and Co KG
Publication of WO2024223298A1 publication Critical patent/WO2024223298A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
    • A61B5/686—Permanently implanted devices, e.g. pacemakers, other stimulators, biochips
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • BIOTRONIK SE & Co. KG Applicant: BIOTRONIK SE & Co. KG
  • the invention relates to a sensor system for implantation under the skin or for attachment to the skin of a patient for evaluating a physiology of a post-infarction patient.
  • the invention relates to a computer-implemented method for evaluating a physiology of a post-infarction patient.
  • a sensor system comprising a leadless sensor device for implantation under the skin or for attachment to the skin of a patient for evaluating a physiology of a postinfarction patient having the features of claim 1.
  • the present invention provides a sensor system, in particular a leadless sensor system, comprising a leadless sensor device for implantation under the skin or for attachment to the skin of a patient for evaluating a physiology of a post-infarction patient.
  • the leadless sensor device comprises a power source and at least one sensing unit for recording at least one lead of an electrocardiogram (ECG), also called ECG lead.
  • ECG electrocardiogram
  • An ECG lead is a graphical description of the electrical activity of the heart.
  • the leadless sensor device comprises a communication interface for direct or indirect communication of the ECG data to a data acquisition system external to the patient body, and an evaluation unit arranged in the leadless sensor device and/or in the data acquisition system, wherein the evaluation unit is configured to detect a Q-wave in the ECG lead and to analyze pathological indicators of the Q-wave.
  • the present invention provides a computer-implemented method for evaluating a physiology of a post-infarction patient.
  • the method comprises providing a sensor system, in particular a leadless sensor system, comprising a leadless sensor device, the leadless sensor device comprising a power source and recording at least one ECG lead by means of at least one sensing unit.
  • the method comprises providing direct or indirect communication of the ECG data to a data acquisition system external to the patient body by means of a communication interface, and detecting a Q-wave in the ECG lead and analyzing pathological indicators of the Q-wave by means of an evaluation unit arranged in the leadless sensor device and/or in the data acquisition system.
  • the Q-wave is the first wave of the QRS-complex in the electrocardiogram. It points in the opposite direction of the R-wave.
  • the Q-wave results from retrograde excitation propagation, i.e., propagation from the apex to the base of the heart, which occurs primarily in the papillary muscles and septum interventriculare.
  • the Q-waveform usually occurs only in leads with high R-waveforms, for example, lead I and aVL in a left type or lead II and III, and aVF in a right type or a steep type. It usually has a width of up to 0.03 seconds and a depth of about 0.2-0.3 mV. However, in aVR and Nehb D, its amplitude can be much more pronounced.
  • pathologic Q The widened and deepened Q-wave is considered a pathological change. It is referred to as pathologic Q or Pardee-Q. Pathologic Q occurs when the width of the Q-wave is greater than 0.04 seconds and its depth is greater than 14 of the highest R-wave.
  • Pathologic Q occurs in the limb leads in the following situations, for example a pronounced anterior wall infarction, a posterior wall infarction and/or a heart wall aneurysm.
  • An idea of the present invention is thus to allow telemedical follow-up of post-infarction patients by recording and analyzing the myocardial recovery and transmitting it telemedically.
  • the leadless sensor device is configured to be implanted under the skin or attached to the skin of the patient in such a way that an individual ECG, in which at least one infarct-related, pathological indicator of the Q-wave, in particular a Q-wave change, is present and/or expected, is detectable.
  • the Q-wave in the ECG lead can be detected and pathological indicators of the Q-wave can be analyzed by the evaluation unit.
  • the evaluation unit is configured to analyze a width of the Q-wave, to evaluate a T-wave, a ST-segment and/or to analyze an amplitude of the Q-wave in relation to an amplitude of an R-wave of a same QRS complex.
  • the leadless sensor device comprises a memory unit configured to record Q-wave data between interrogations and/or transmissions of the leadless sensor device.
  • the recorded Q-wave data can thus be sent to the evaluation unit for further analysis.
  • an evaluation and/or the transmission of the Q-wave data is performed at predetermined intervals, in particular hourly, daily and/or weekly intervals.
  • the evaluation and/or transmission of the recorded Q-wave data thus enables a close followup of the patient, wherein a therapy can be designed and/or adapted in line with analysis results of the recorded Q-wave data.
  • the leadless sensor device is configured to record at least one parameter representing a physical load on the body of the patient.
  • Such a parameter can be an acceleration rate of the patient body recorded by the leadless sensor device.
  • This parameter can be used as a further input to the evaluation unit which enables a determination whether or not external factors such as a physical load on the body of the patient influence the Q-wave data.
  • the evaluation unit is configured to evaluate the Q-wave data when the physical load on the body of the patient exceeds a predetermined threshold value.
  • the communication interface is configured to communicate with the data acquisition system via a coil communication, a galvanic communication method, RF-telemetry in a frequency range between 500 MHz and 1 GHz, RF telemetry in a frequency range above 1 GHz and/or via an additional leadless sensor device configured to act as a relay station.
  • the communication interface thus provides a wide range of communication methods hence offering a broad compatibility with the data acquisition systems.
  • the power source of the leadless sensor device is configured to provide a run time of the leadless sensor device of between three months and one year. This time frame advantageously covers a patient follow-up period.
  • the power source and/or power usage of the leadless sensor device can be adapted to the intended patient follow-up period.
  • the evaluation unit is configured to evaluate the Q-wave morphology using a machine learning algorithm.
  • the machine learning algorithm advantageously enables to identify non-linear correlations of the Q-wave data to pathological changes in the patient physiology.
  • the leadless sensor device if a recovery or non-recovery of the patient physiology is detected, the leadless sensor device is configured to be switched from a first operating mode to a second operating mode, in particular a message mode for third party devices and/or an active or passive monitoring mode.
  • a change or non-change in patient physiology is an important indicator whether or not patient recovery is in line with expectations and/or therapy goals.
  • the leadless sensor device is configured to determine if a detection is identical in different operating modes comprising a sitting mode, a standing mode, a running mode and/or a walking mode and/or using information on whether medication was administered during a predetermined interval prior to the detection.
  • a patient reaction to medical intake can be monitored by detecting changes in patient physiology. Furthermore, the influence of external factors such as physical exercise on patient physiology can be taken into account when evaluating ECG data.
  • the leadless sensor device is configured to connect to a wearable device comprising an oxygen sensor to determine if the detected recovery or non-recovery of the patient physiology is due to a blood oxygen level being below a predetermined threshold value and/or wherein the leadless sensor device is configured to calculate a probability of a recurrent or new infarct.
  • a wearable device comprising an oxygen sensor to determine if the detected recovery or non-recovery of the patient physiology is due to a blood oxygen level being below a predetermined threshold value and/or wherein the leadless sensor device is configured to calculate a probability of a recurrent or new infarct.
  • the leadless sensor device is configured to classify a condition of the patient physiology, wherein depending on a classification result, the leadless sensor device is set into an associated operating mode.
  • the operating mode can be a messaging mode informing the patient and/or healthcare provider of the data analysis and/or providing a recommended action to the patient such as a medication intake, a physical activity and/or scheduling an appointment with the healthcare provider in charge of patient follow-up.
  • the herein described features of the leadless sensor device for implantation under the skin or for attachment to the skin of a patient for evaluating a physiology of a post-infarction patient are also disclosed for the computer-implemented method for evaluating a physiology of a post-infarction patient and vice versa.
  • Fig. 1 shows a diagram of a sensor system for evaluating a physiology of a postinfarction patient according to a preferred embodiment of the invention
  • Fig. 2 shows a flowchart of a computer-implemented method for evaluating a physiology of a post-infarction patient according to the preferred embodiment of the invention
  • Fig.3 shows a progression of a Q waveform in a myocardial infarction-follow-up patient according to the preferred embodiment of the invention.
  • the (leadless) sensor system 1 shown in Fig. 1 comprises a leadless sensor device 10 and a data acquisition system 16 external to the patient body.
  • the leadless sensor device 10 for implantation under the skin or for attachment to the skin of a patient for evaluating a physiology of a post-infarction patient shown in Fig. 1 comprises a power source 12 and at least one sensing unit 14 for recording at least one ECG lead 15.
  • the leadless sensor device 10 comprises a communication interface 17 for direct or indirect communication of the ECG data to the data acquisition system 16 external to the patient body and an evaluation unit 18 arranged in the data acquisition system 16, wherein the evaluation unit 18 is configured to detect a Q-wave in the ECG lead 15 and to analyze pathological indicators of the Q-wave.
  • the evaluation unit 18 can be arranged in the leadless sensor device 10 or in both the data acquisition system 16 and the leadless sensor device 10.
  • the sensor system 1 of Fig. 1 for telemedical monitoring further comprises a patient device.
  • a patient wears a leadless sensor device 10, in particular a subcutaneously implantable sensor, whose implantation location depends on the affected infarct area of the heart.
  • the leadless sensor device 10 transmits Q-wave information to a remote physician follow-up system via a patient device.
  • a preferred implementation of the Q-wave information is to determine the amplitude ratio of the Q-wave and R-wave in predetermined intervals, e.g. daily. Such information can be presented as a trend and used for follow-up.
  • the leadless sensor device 10 is configured to be implanted under the skin or attached to the skin of the patient in such a way that an individual ECG, in which at least one infarct-related, pathological indicator of the Q-wave, in particular a Q-wave change, is present and/or expected, is detectable.
  • the evaluation unit 18 is configured to analyze a width of the Q-wave, to evaluate a T-wave, a ST-segment and/or to analyze an amplitude of the Q-wave in relation to an amplitude of an R-wave of a same QRS complex.
  • the leadless sensor device 10 moreover comprises a memory unit 20 configured to record Q-wave data D between interrogations and/or transmissions of the leadless sensor device 10.
  • An evaluation and/or the transmission of the Q-wave data D is performed at predetermined intervals, in particular hourly, daily and/or weekly intervals.
  • the leadless sensor device 10 is configured to record at least one parameter 22 representing a physical load on the body of the patient.
  • the evaluation unit 18 is configured to evaluate the Q-wave data D when the physical load on the body of the patient exceeds a predetermined threshold value.
  • the communication interface 17 is configured to communicate with the data acquisition system 16 via a coil communication, a galvanic communication method, RF -telemetry in a frequency range between 500 MHz and 1 GHz, RF telemetry in a frequency range above 1 GHz and/or via an additional leadless sensor device 10 configured to act as a relay station.
  • the data acquisition system 16 is further configured to transmit the Q-wave data D, an evaluation result of the Q-wave data D and/or a message triggered by the data acquisition system 16 or the evaluation unit 18 in response to the evaluation of the Q-wave data D to a remote follow-up system 28, in particular a follow-up system of a healthcare provider.
  • the power source 12 of the leadless sensor device 10 is configured to provide a run time of the leadless sensor device 10 of between three months and one year. Furthermore, the evaluation unit 18 is configured to evaluate the Q-wave morphology using a machine learning algorithm.
  • the machine learning algorithm is configured to learn across patients or on a patient-by- patient basis.
  • the machine learning algorithm is trained using labeled ground truth data as well as said Q- wave data D obtained by the leadless sensor device 10.
  • the model can further process oxygen/respiratory data and/or parameters representing a load on the patient body.
  • the machine learning algorithm is further configured to identify a patient group to which the patient belongs according to a QRS-data history and which measures are sensibly initiated for said group.
  • the leadless sensor device 10 is configured to be switched from a first operating mode Ml to a second operating mode M2, in particular a message mode for third party devices and/or an active or passive monitoring mode.
  • the leadless sensor device 10 is further configured to determine if a detection is identical in different operating modes comprising a sitting mode, a standing mode, a running mode and/or a walking mode and/or using information on whether medication was administered during a predetermined interval prior to the detection.
  • the leadless sensor device 10 is configured to connect to a wearable device 24 comprising an oxygen sensor 26 to determine if the detected recovery or non-recovery of the patient physiology is due to a blood oxygen level being below a predetermined threshold value and/or wherein the leadless sensor device 10 is configured to calculate a probability of a recurrent or new infarct.
  • Possible conditions are e.g., a non-recovery of the patient physiology is detected and the blood oxygen level being is below the predetermined threshold value. This would indicate a critical patient condition.
  • a non-recovery of the patient physiology is detected and the blood oxygen level is above the predetermined threshold value. In this case further observation of the patient physiology would be advisable.
  • a different operating mode of the leadless sensor device 10 is set.
  • the leadless sensor device 10 is configured to classify a condition of the patient physiology, wherein depending on a classification result, the leadless sensor device 10 is set into an associated operating mode.
  • Fig. 2 shows a flowchart of a computer-implemented method for evaluating a physiology of a post-infarction patient according to the preferred embodiment of the invention.
  • the method for evaluating a physiology of a post-infarction patient comprises providing a sensor system 1 comprising a leadless sensor device 10, the leadless sensor device 10 comprising a power source 12 and recording at least one ECG lead 15 by means of at least one sensing unit 14.
  • the method comprises providing direct or indirect communication of the ECG data to a data acquisition system 16 external to the patient body by means of a communication interface 17, and detecting a Q-wave in the ECG lead 15 and analyzing pathological indicators of the Q-wave by means of an evaluation unit 18 arranged in the leadless sensor device 10 and/or in the data acquisition system 16.
  • pathological Q and its regression can provide an indication of the recovery of a post-myocardial infarction patient and can therefore be used for follow-up.
  • Fig. 3 shows the progression of the Q waveform in a patient who is progressively recovering from a myocardial infarction.
  • a pronounced Q-wave A is still evident at first, which is larger in amplitude than the following R-wave. Over time, this pathological Q-wave decreases in size B and subsequently normalizes C.
  • the amplitude of the Q- wave in relation to the R-wave amplitude should preferably be determined and evaluated e.g., as a trend function.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
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  • Animal Behavior & Ethology (AREA)
  • Biophysics (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Cardiology (AREA)
  • Artificial Intelligence (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un système de capteur (1) comprenant un dispositif de capteur sans fil (10) destiné à être implanté sous la peau ou destiné à être fixé sur la peau d'un patient pour évaluer une physiologie d'un patient post-infarctus, le dispositif de capteur sans fil (10) comprenant une source d'alimentation (12), au moins une unité de détection (14) pour enregistrer au moins un conducteur d'ECG (15), une interface de communication (17) pour une communication directe ou indirecte des données d'ECG à un système d'acquisition de données (16) externe au corps de patient, et une unité d'évaluation (18) disposée dans le dispositif de capteur sans fil (10) et/ou dans le système d'acquisition de données (16), l'unité d'évaluation (18) étant conçue pour détecter une onde Q dans le conducteur d'ECG (15) et pour analyser des indicateurs pathologiques de l'onde Q. En outre, l'invention concerne un procédé mis en œuvre par ordinateur pour évaluer une physiologie d'un patient post-infarctus.
PCT/EP2024/059711 2023-04-25 2024-04-10 Système de capteur et procédé mis en œuvre par ordinateur pour évaluer une physiologie d'un patient post-infarctus Pending WO2024223298A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP23169643.6 2023-04-25
EP23169643 2023-04-25

Publications (1)

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WO2024223298A1 true WO2024223298A1 (fr) 2024-10-31

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020188214A1 (en) * 2001-06-01 2002-12-12 Misczynski Dale Julian System and process for analyzing a medical condition of a user
US20140371832A1 (en) * 2013-06-12 2014-12-18 Subham Ghosh Implantable electrode location selection
US20170079598A1 (en) * 2015-09-22 2017-03-23 Cardiac Pacemakers, Inc. Systems and methods for monitoring autonomic health
US20220133168A1 (en) * 2020-11-02 2022-05-05 Medtronic, Inc. Electrocardiogram gain adjustment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020188214A1 (en) * 2001-06-01 2002-12-12 Misczynski Dale Julian System and process for analyzing a medical condition of a user
US20140371832A1 (en) * 2013-06-12 2014-12-18 Subham Ghosh Implantable electrode location selection
US20170079598A1 (en) * 2015-09-22 2017-03-23 Cardiac Pacemakers, Inc. Systems and methods for monitoring autonomic health
US20220133168A1 (en) * 2020-11-02 2022-05-05 Medtronic, Inc. Electrocardiogram gain adjustment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YILDIRIM OZAL ET AL: "Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis", 2021 COMPUTING IN CARDIOLOGY (CINC), CREATIVE COMMONS, vol. 48, 13 September 2021 (2021-09-13), pages 1 - 4, XP033999401, DOI: 10.23919/CINC53138.2021.9662825 *

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