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WO2025224669A1 - État glycémique courant basé sur un signal cardiaque - Google Patents

État glycémique courant basé sur un signal cardiaque

Info

Publication number
WO2025224669A1
WO2025224669A1 PCT/IB2025/054281 IB2025054281W WO2025224669A1 WO 2025224669 A1 WO2025224669 A1 WO 2025224669A1 IB 2025054281 W IB2025054281 W IB 2025054281W WO 2025224669 A1 WO2025224669 A1 WO 2025224669A1
Authority
WO
WIPO (PCT)
Prior art keywords
cardiac signal
processing circuitry
patient
wave
examples
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/IB2025/054281
Other languages
English (en)
Inventor
Niranjan Chakravarthy
Rodolphe Katra
Sean R. LANDMAN
Tyler W. STIGEN
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.)
Medtronic Inc
Original Assignee
Medtronic Inc
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 Medtronic Inc filed Critical Medtronic Inc
Publication of WO2025224669A1 publication Critical patent/WO2025224669A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • A61B5/355—Detecting T-waves
    • 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
    • A61B5/36—Detecting PQ interval, PR interval or QT interval
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0204—Operational features of power management
    • A61B2560/0209—Operational features of power management adapted for power saving
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Definitions

  • This disclosure generally relates to medical devices and, more particularly, to patient monitoring devices.
  • a variety of medical devices have been proposed for therapeutically treating or monitoring cardiac, neurological, and/or other conditions of a patient.
  • Medical devices may monitor physiological signals of the patient.
  • a medical device may monitor a cardiac signal, e.g., an electrocardiogram (ECG) signal or a cardiac electrogram (EGM) signal, of the patient to monitor a patient condition.
  • ECG electrocardiogram
  • EMM cardiac electrogram
  • using these signals such devices facilitate monitoring and evaluating patient health over a number of months or years, outside of a clinic setting.
  • the disclosure describes techniques for facilitating monitoring of patient cardiac episodes and glycemic state by monitoring a physiological signal of the patient, such as a cardiac signal, e.g., an electrocardiogram (ECG) signal or a cardiac electrogram (EGM) signal.
  • a cardiac signal e.g., an electrocardiogram (ECG) signal or a cardiac electrogram (EGM) signal.
  • ECG electrocardiogram
  • EMM cardiac electrogram
  • the example techniques for monitoring patient cardiac episodes and glycemic state include continuously monitoring the cardiac signal and storing, i.e., capturing, segments of the cardiac signal based on one or more features of the cardiac signal satisfying one or more criterion.
  • a computing system separate from the medical device used to sense the cardiac signal, may be configured to determine the glycemic state of the patient based on the cardiac signal.
  • this disclosure describes example techniques in which the medical device may determine whether a cardiac signal data segment of the cardiac signal satisfies certain criteria and/or an amount of time the cardiac signal has been indicative of a current actionable glycemic state finding satisfies a time threshold and transmit that cardiac signal data segment based on the cardiac signal data segment satisfying the criteria and/or the amount of time the cardiac signal has been indicative of the current actionable glycemic state finding satisfies the time threshold.
  • the time threshold may be a first time threshold, and immediately subsequent time thresholds may be, e.g., lower, than the first time threshold.
  • the first threshold may be 30 minutes.
  • An immediately subsequent threshold may be 10 minutes.
  • the medical device itself may be configured to determine the glycemic state of the patient. In such examples, by determining the cardiac signal data segment that satisfied the criteria, the medical device may determine a current glycemic condition without expending resources of determining glycemic state for all segments of the cardiac signal.
  • Examples of the criteria may include features of the cardiac signal satisfying a threshold for a certain threshold amount of time.
  • a medical device of a system monitoring the cardiac signal may be configured to monitor one or more features of the cardiac signal, e.g., a feature of the cardiac signal, over time.
  • the system may be configured to compare the one or more features to one or more corresponding thresholds. Based on the one or more features meeting the one or more corresponding thresholds for a threshold period of time, the system may determine to capture, i.e., store, a segment of the cardiac signal. Based on the cardiac signal segment, the system may determine a glycemic state of the patient.
  • a system includes a medical device, the medical device including: sensing circuitry configured to sense a cardiac signal of a patient; and processing circuitry configured to: compare one or more features of the cardiac signal to one or more corresponding feature thresholds; compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold; determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and output the determined cardiac signal data segment.
  • a method includes: sensing, by sensing circuitry of a medical device of a system, a cardiac signal of a patient; comparing, by processing circuitry of the medical device, one or more features of the cardiac signal to one or more corresponding thresholds; comparing, by the processing circuitry, an amount of time the one or more features have met the one or more corresponding thresholds to a time threshold; determining, by the processing circuitry, a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and outputting, by the processing circuitry, the determined cardiac signal data segment.
  • a non-transitory computer-readable medium stores instructions that, when executed by processing circuitry, cause the processing circuitry to: compare one or more features of the cardiac signal to one or more corresponding feature thresholds; compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold; determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and output the determined cardiac signal data segment.
  • FIG. 1 illustrates the environment of an example medical device system in conjunction with a patient, in accordance with one or more techniques of this disclosure.
  • FIGS. 2A and 2B are conceptual diagrams illustrating example implantable medical devices that operate in accordance with one or more techniques of this disclosure.
  • FIG. 3 is a block diagram illustrating an example configuration of a medical device that operates in accordance with one or more techniques of the present disclosure.
  • FIG. 4 is a block diagram illustrating an example configuration of the computing system of FIG. 1, in accordance with one or more techniques of this disclosure.
  • FIG. 5 is a flow diagram illustrating an example operation for determining to send a transmission, in accordance with one or more techniques of this disclosure.
  • FIG. 6 is a flow diagram illustrating an example operation for periodically determining a glycemic state of a patient, in accordance with one or more techniques of this disclosure.
  • FIG. 7 is a flow diagram illustrating an example operation for adjusting one or more monitoring and/or transmission parameters based on a risk of diabetes, in accordance with one or more techniques of this disclosure.
  • FIG. 8 is a flow diagram illustrating an example operation for adjusting one or more monitoring and/or transmission parameters based on user input, in accordance with one or more techniques of this disclosure.
  • FIG. 9 is a conceptual diagram illustrating an example machine learning model configured to determine a glycemic state of a patient, in accordance with one or more techniques of this disclosure.
  • FIG. 10 is a conceptual diagram illustrating an example training process for a machine learning model, in accordance with one or more techniques of this disclosure.
  • a variety of types of implantable and external devices are configured to monitor health based on sensed cardiac signals, e.g., electrocardiogram (ECG) signals or cardiac electrogram (EGM) signals, and, in some cases, other physiological signals.
  • ECG electrocardiogram
  • EMM cardiac electrogram
  • External devices that may be used to non-invasively sense and monitor cardiac signals and other physiological signals include wearable devices with electrodes configured to contact the skin of the patient, such as watches, patches, rings, necklaces, hearing aids, a wearable cardiac monitor or automated external defibrillator (AED), clothing, car seats, or bed linens.
  • Such external devices may facilitate relatively longer-term monitoring of patient health during normal daily activities.
  • Implantable medical devices also sense and monitor cardiac signals and other physiological signals and detect health events such as episodes of arrhythmia, cardiac arrest, myocardial infarction, stroke, and seizure.
  • Example IMDs include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. Some IMDs do not provide therapy, such as implantable patient monitors.
  • One example of such an IMD is the Reveal LINQTM or LINQ IITM insertable cardiac monitors (ICMs), available from Medtronic, Inc., which may be inserted subcutaneously.
  • Such IMDs may facilitate relatively longer-term continuous monitoring of patients during normal daily activities, and may periodically or on demand transmit collected data, e.g., episode data for detected arrhythmia episodes or other health episodes and/or potential indicators of health issues, to a remote patient monitoring system, such as the Medtronic CareLinkTM Network via a home monitoring system or a smart phone application.
  • collected data e.g., episode data for detected arrhythmia episodes or other health episodes and/or potential indicators of health issues
  • a remote patient monitoring system such as the Medtronic CareLinkTM Network via a home monitoring system or a smart phone application.
  • This disclosure relates to techniques for monitoring patient physiological signals, such as cardiac signals, photoplethysmography (PPG) signals, accelerometer signals, pressure signals, and/or impedance cardiography signals, to facilitate monitoring of cardiac episodes and/or glycemic state of a patient.
  • Devices may be configured to detect episodes based on the cardiac signal, such as fibrillations or arrhythmias.
  • cardiac signals can be used to estimate the glycemic state of a patient.
  • patients with certain cardiac conditions e.g., heart failure (HF)
  • HF heart failure
  • patients with diabetes experience or have an increased risk of experiencing certain cardiac conditions.
  • the techniques of this disclosure may facilitate identification of changes in patient conditions, such as the onset of comorbid conditions, which may improve patient outcomes.
  • Current methods of monitoring glycemic state with cardiac data oftentimes include capturing physiological signals, e.g., cardiac signals, with no actionable findings via a medical device and processing the cardiac signal data, e.g., by implementing a machine learning model, and/or transmitting the cardiac signal data, both of which may which can decrease the battery life of the device.
  • the techniques of this disclosure include determining whether one or more features of the cardiac signal are indicative of a potentially actionable glycemic state finding before determining the glycemic state, which may be relatively complex to determine. By determining whether to determine glycemic state based on whether the cardiac signal is indicative of a potentially actionable glycemic state finding, the techniques of this disclosure may conserve battery life, which may increase device longevity.
  • a first device e.g., medical device
  • a second device e.g., computing system
  • the power consumption of the first device may increase by an undesired amount. If the first device were to transmit less frequently (e.g., once a day) to conserve power, then there may be reduced chance of identifying a current actionable glycemic state finding.
  • the first device may both sense the cardiac signal and determine the glycemic state (e.g., such as examples in which the first device is a watch). Similar to above, if the first device were to continuously determine the glycemic state, the power consumption of the first device may increase by an undesired amount. If the first device were to determine the glycemic state less frequently (e.g., every few hours or only upon patient request), then there may be reduced chance of identifying a current actionable glycemic state finding.
  • This disclosure describes example techniques to determine whether certain features of the cardiac signal satisfy a threshold for a threshold amount of time.
  • the thresholds may be preset, selected, or updated such that a cardiac signal data segment that satisfies the threshold is likely to indicate whether the patient is currently experiencing an actionable glycemic state. Accordingly, the frequency of the transmission of cardiac signal data and/or the frequency of the determination of glycemic state may be reduced to conserve power, while ensuring that cardiac signal data that are likely provide actionable glycemic state findings are analyzed to determine the glycemic state of the patient.
  • the periodic transmissions (e.g., daily) of physiological signals may supplement transmissions of potentially actionable captured cardiac signals, PPG signals, accelerometer signals, pressure signals, and/or impedance cardiography signals.
  • the techniques further include determining one or more trends in patient glycemic state, e.g., based on one or more of periodic transmissions of cardiac signals or potentially actionable cardiac signals. By identifying trends in patient glycemic state over time, the techniques of this disclosure may facilitate identification of changes in patient condition. By facilitating identification of changes in patient condition, such as an onset of diabetes or a change in a risk of diabetes, the techniques of this disclosure may allow a patient or user to address the change in patient condition, which may improve patient outcomes.
  • the system may be configured to adjust the periodic schedule for determining glycemic state. Additionally, or alternatively, the system may be configured to adjust one or more monitoring and/or transmission parameters to detecting and transmitting potentially actionable physiological signals, e.g., potentially actionable cardiac signals.
  • the techniques of this disclosure may be patient-specific, which may increase an accuracy of glycemic state determinations, thereby improving patient outcomes.
  • the techniques of this disclosure may implement (e.g., execute) a machine learning model.
  • the techniques may improve an accuracy of glycemic state determination. Additionally, by implementing a machine learning model, the techniques may decrease clinician review time.
  • FIG. 1 illustrates the environment of an example medical device system in conjunction with a patient, in accordance with one or more techniques of this disclosure.
  • the example techniques may be used with a medical device 10, which may be in wireless communication with an external device 12.
  • medical device 10 is an implantable medical device (IMD) implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1.
  • Medical device 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette.
  • Medical device 10 includes a plurality of electrodes (not shown in FIG. 1) and is configured to sense a cardiac signal via the plurality of electrodes.
  • medical device 10 takes the form of the Reveal LINQTM or LINQ IITM insertable cardiac monitor (ICM).
  • ICM Reveal LINQTM or LINQ IITM insertable cardiac monitor
  • External device 12 is configured for wireless communication with medical device 10.
  • External device 12 may be configured to communicate with computing system 8 via network 16.
  • external device 12 may provide a user interface and allow a user to interact with medical device 10.
  • Computing system 8 may comprise external devices configured to allow the user to interact with medical device 10, or data collected from medical device 10, via network 16.
  • External device 12 may be used to retrieve data from medical device 10 and may transmit the data to computing system 8 via network 16.
  • the retrieved data may include cardiac signal data collected by medical device 10 and/or other physiological signals, e.g., PPG data, accelerometer data, pressure data, and/or impedance cardiography data, recorded by medical device 10.
  • the retrieved data may include captured cardiac signal data, i.e., cardiac signal segments recorded by medical device 10, e.g., on a regular schedule or due to medical device 10 identifying sustained changes in one or more features of the cardiac signal.
  • the retrieved data may additionally or alternatively include captured PPG signal data, i.e., PPG segments recorded by medical device 10, e.g., on a regular schedule or due to medical device 10 identifying sustained changes in one or more features of the PPG signal, and/or impedance cardiography signal data, i.e., impedance cardiography segments recorded by medical device 10, e.g., on a regular schedule or due to medical device 10 identifying sustained changes in one or more features of the impedance cardiography signal.
  • captured PPG signal data i.e., PPG segments recorded by medical device 10, e.g., on a regular schedule or due to medical device 10 identifying sustained changes in one or more features of the PPG signal
  • impedance cardiography signal data i.e., impedance cardiography segments recorded by medical device 10, e.g., on a regular schedule or due to medical device 10 identifying sustained changes in one or more features of the impedance cardiography signal.
  • medical device 10 may be configured for wireless communication with computing system 8 via network 16, i.e., medical device 10 may perform one or more of the operations described herein as being performed by external device 12.
  • computing system 8 includes one or more handheld external devices, computer workstations, servers, or other networked external devices.
  • computing system 8 may include one or more devices, including processing circuitry and storage devices, which may be distributed across multiple locations and/or multiple servers.
  • Computing system 8 may comprise a cloud computing system.
  • Computing system 8 and network 16 may be implemented by the Medtronic CareLinkTM Network or other patient monitoring system, in some examples.
  • Computing system 8 may implement a monitoring system (not shown) that may analyze cardiac data received from medical devices, including medical device 10, and direct the cardiac data to reviewers.
  • Computing system 8 may implement machine learning models for analysis of cardiac data.
  • the machine learning models may include neural networks, deep learning models, convolutional neural networks, or other types of predictive analytics systems.
  • Network 16 may include one or more external devices (not shown), such as one or more non-edge switches, routers, hubs, gateways, security devices such as firewalls, intrusion detection, and/or intrusion prevention devices, servers, computer terminals, laptops, printers, databases, wireless mobile devices such as cellular phones or personal digital assistants, wireless access points, bridges, cable modems, application accelerators, or other network devices.
  • Network 16 may include one or more networks administered by service providers and may thus form part of a large-scale public network infrastructure, e.g., the Internet.
  • Network 16 may provide external devices, such as computing system 8 and medical device 10, access to the Internet, and may provide a communication framework that allows the external devices to communicate with one another.
  • network 16 may be a private network that provides a communication framework that allows computing system 8, medical device 10, and/or external device 12 to communicate with one another but isolates one or more of computing system 8, medical device 10, or external device 12 from devices external to network 16 for security purposes.
  • the communications between computing system 8, medical device 10, and external device 12 are encrypted.
  • Computing system 8 is an example of a computing system configured to receive cardiac data stored by a medical device, e.g., medical device 10, of a patient, e.g., patient 4.
  • Computing system 8 may be managed by a manufacturer of medical device 10 to, for example, provide cloud storage and analysis of collected data, maintenance and software services, or other networked functionality for their devices and users thereof.
  • External device 12 may transmit data, including cardiac data and/or other physiological data retrieved from medical device 10, to computing system 8 via network 16.
  • the cardiac data and/or other physiological data may include, for example, baseline cardiac data, episode cardiac data, e.g., AF cardiac data or ventricular tachycardia cardiac data, patient-activated cardiac data, and other physiological signals or data recorded by IMD 10 and/or external device(s) 12.
  • Computing system 8 may also retrieve data regarding patient 4 from one or more sources of electronic health records (EHR) via network 16.
  • EHR may include data regarding historical transmission data, previous health events and treatments, disease states, comorbidities, demographics, height, weight, and body mass index (BMI), as examples, of patients including patient 4.
  • BMI body mass index
  • Computing system 8 may use data from EHR to configure algorithms implemented by medical device 10, external device 12, and or computing system 8 to determine glycemic state of the patient and/or monitor a cardiac condition of patient 4.
  • computing system 8 data from EHR to external device 12 and/or medical device 10 for storage therein and use as part of their algorithms for detecting episodes and/or determining glycemic state.
  • this disclosure describes example techniques of determining an overall glycemic state of the patient based on a physiological signal segment, e.g., a cardiac signal segment, a PPG signal segment, an accelerometer signal segment, a pressure signal segment, and/or an impedance cardiography signal segment, determined based on determining one or more features of the physiological signal meeting one or more corresponding feature thresholds for a threshold duration of time.
  • a physiological signal segment e.g., a cardiac signal segment, a PPG signal segment, an accelerometer signal segment, a pressure signal segment, and/or an impedance cardiography signal segment, determined based on determining one or more features of the physiological signal meeting one or more corresponding feature thresholds for a threshold duration of time.
  • the example techniques may be performed using any combination of the components illustrated in FIG. 1. In examples where fewer components than those illustrated in FIG. 1 are used to determine the overall glycemic state, the remaining components may not be necessary.
  • medical device 10 may sense the cardiac signal. However, rather than using medical device 10, it may be possible for external device 12 (e.g., such as where external device 12 is a watch) to sense the cardiac signal. In such an example, medical device 10 may not be needed.
  • external device 12 e.g., such as where external device 12 is a watch
  • processing circuitry of medical device 10 may be configured to compare one or more features of the cardiac signal to one or more corresponding feature thresholds and determine whether an amount of time the one or more features have met the one or more corresponding feature thresholds. Medical device 10 may then determine a cardiac signal data segment from the cardiac signal and transmit the cardiac signal data segment and/or the one or more features of the cardiac signal to computing system 8 through external device 12 via network 16. However, in some examples, external device 12 may compare the one or more features to the one or more corresponding feature thresholds and/or determine whether the amount of time the one or more features have met the one or more corresponding feature thresholds, rather than medical device 10.
  • processing circuitry of medical device 10 or external device 12 may determine a cardiac signal data segment based on a comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold and may transmit the cardiac signal data segment and/or the one or more features of the cardiac signal to computing system 8.
  • Computing system 8 may be configured to determine the overall glycemic state of the patient based on the cardiac signal data segment and/or based on the one or more features of the cardiac signal. In some examples, computing system 8 may additionally determine the overall glycemic state based on previous device-detected glycemic state- related features. For instance, computing system 8 may execute a trained machine learning model, and the input to the trained machine learning model may be the cardiac signal data segment (e.g., features of the cardiac signal segment) and/or the one or more features of the cardiac signal. However, it may be possible for external device 12 to determine the overall glycemic state. In some examples, medical device 10 may be configured to determine the overall glycemic state.
  • the example techniques described in this disclosure may be performed by one or any combination of devices and systems illustrated in FIG. 1.
  • the processing circuitry may be located within one of the illustrated components (e.g., computing system 8) or may be distributed across the components.
  • the processing circuitry may be considered as having different portions, where a first portion is located within medical device 10, a second portion is located within computing system 8, etc.
  • a first portion of the processing circuitry within medical device 10 may determine the cardiac signal data segment
  • a second portion of the processing circuitry within computing system 8 may determine the overall glycemic state.
  • FIG. 2A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of medical device 10 of FIG. 1 as an ICM.
  • IMD 10A may be embodied as a monitoring device having housing 212, proximal electrode 216A and distal electrode 216B.
  • Housing 212 may further comprise first major surface 214, second major surface 218, proximal end 220, and distal end 222.
  • Housing 212 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids.
  • Housing 212 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 216A and 216B.
  • IMD 10A is defined by a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D.
  • the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion.
  • the device shown in FIG. 2A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion.
  • the spacing between proximal electrode 216A and distal electrode 216B may range from 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 5 mm to 60 mm.
  • IMD 10A may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm.
  • the width W of major surface 214 may range from 3 mm to 15, mm, from 3 mm to 10 mm, or from 5 mm to 15 mm, and may be any single or range of widths between 3 mm and 15 mm.
  • the thickness of depth D of IMD 10A may range from 2 mm to 15 mm, from 2 mm to 9 mm, from 2 mm to 5 mm, from 5 mm to 15 mm, and may be any single or range of depths between 2 mm and 15 mm.
  • IMD 10A according to an example of the present disclosure has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters.
  • the first major surface 214 faces outward, toward the skin of the patient while the second major surface 218 is located opposite the first major surface 214.
  • proximal end 220 and distal end 222 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
  • IMD 10A including instrument and method for inserting IMD 10A is described, for example, in U.S. Patent No. 11,311,312, incorporated herein by reference in its entirety.
  • Proximal electrode 216A is at or proximate to proximal end 220, and distal electrode 216B is at or proximate to distal end 222.
  • Proximal electrode 216A and distal electrode 216B are used to sense cardiac signals thoracically outside the ribcage, which may be implanted sub-muscularly or subcutaneously.
  • Cardiac signals may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 230 A to another device, which may be another implantable device or an external device, such as external device 12.
  • electrodes 216A and 216B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an electroencephalogram (EEG), electromyogram (EMG), or a nerve signal, or for measuring impedance, from any implanted location.
  • EEG electroencephalogram
  • EMG electromyogram
  • nerve signal or for measuring impedance, from any implanted location.
  • proximal electrode 216A is at or in close proximity to the proximal end 220 and distal electrode 216B is at or in close proximity to distal end 222.
  • distal electrode 216B is not limited to a flattened, outward facing surface, but may extend from first major surface 214 around rounded edges 224 and/or end surface 226 and onto the second major surface 218 so that the electrode 216B has a three-dimensional curved configuration.
  • electrode 216B is an uninsulated portion of a metallic, e.g., titanium, part of housing 212.
  • proximal electrode 216A is located on first major surface 214 and is substantially flat, and outward facing.
  • proximal electrode 216A may utilize the three-dimensional curved configuration of distal electrode 216B, providing a three-dimensional proximal electrode (not shown in this example).
  • distal electrode 216B may utilize a substantially flat, outward facing electrode located on first major surface 214 similar to that shown with respect to proximal electrode 216A.
  • proximal electrode 216A and distal electrode 216B are located on both first major surface 214 and second major surface 218.
  • proximal electrode 216A and distal electrode 216B are located on both major surfaces 214 and 218, and in still other configurations both proximal electrode 216A and distal electrode 216B are located on one of the first major surface 214 or the second major surface 218 (e.g., proximal electrode 216A located on first major surface 214 while distal electrode 216B is located on second major surface 218).
  • IMD 10A may include electrodes on both major surface 214 and 218 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A.
  • Electrodes 216A and 216B may be formed of a plurality of different types of biocompatible conductive material, e.g., stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
  • proximal end 220 includes a header assembly 228 that includes one or more of proximal electrode 216A, integrated antenna 230A, anti-migration projections 232, and/or suture hole 234.
  • Integrated antenna 230A is located on the same major surface (i.e., first major surface 214) as proximal electrode 216A and is also included as part of header assembly 228.
  • Integrated antenna 230A allows IMD 10A to transmit and/or receive data.
  • integrated antenna 230 A may be formed on the opposite major surface as proximal electrode 216A or may be incorporated within the housing 212 of IMD 10 A. In the example shown in FIG.
  • antimigration projections 232 are located adjacent to integrated antenna 230A and protrude away from first major surface 214 to prevent longitudinal movement of the device.
  • anti-migration projections 232 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 214.
  • anti -migration projections 232 may be located on the opposite major surface as proximal electrode 216A and/or integrated antenna 230A.
  • header assembly 228 includes suture hole 234, which provides another means of securing IMD 10A to the patient to prevent movement following insertion.
  • header assembly 228 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10 A.
  • FIG. 2B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of medical device 10 from FIG. 1 as an ICM.
  • IMD 10B of FIG. 2B may be configured substantially similarly to IMD 10A of FIG. 2A, with differences between them discussed herein.
  • IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g., an ICM.
  • IMD 10B includes housing having a base 240 and an insulative cover 242.
  • Proximal electrode 216C and distal electrode 216D may be formed or placed on an outer surface of cover 242.
  • Various circuitries and components of IMD 10B may be formed or placed on an inner surface of cover 242, or within base 240.
  • a battery or other power source of IMD 10B may be included within base 240.
  • antenna 230B is formed or placed on the outer surface of cover 242 but may be formed or placed on the inner surface in some examples.
  • insulative cover 242 may be positioned over an open base 240 such that base 240 and cover 242 enclose the circuitries and other components and protect them from fluids such as body fluids.
  • the housing including base 270 and insulative cover 272 may be hermetically sealed and configured for subcutaneous implantation.
  • Circuitries and components may be formed on the inner side of insulative cover 242, such as by using flip-chip technology.
  • Insulative cover 242 may be flipped onto a base 240. When flipped and placed onto base 240, the components of IMD 10B formed on the inner side of insulative cover 242 may be positioned in a gap 244 defined by base 240. Electrodes 216C and 216D and antenna 230B may be electrically connected to circuitry formed on the inner side of insulative cover 242 through one or more vias (not shown) formed through insulative cover 242.
  • Insulative cover 242 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material.
  • Base 240 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 216C and 216D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 216C and 216D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
  • a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
  • the housing of IMD 10B defines a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 2 A.
  • the spacing between proximal electrode 216C and distal electrode 216D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm.
  • IMD 10B may have a length L that ranges from 5 mm to about 70 mm.
  • the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm.
  • the width may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm.
  • the thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm.
  • IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.
  • outer surface of cover 242 faces outward, toward the skin of the patient.
  • proximal end 246 and distal end 248 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
  • edges of IMD 10B may be rounded.
  • FIG. 3 is a block diagram illustrating an example configuration of medical device 10 from FIG. 1 that operates in accordance with one or more techniques of the present disclosure.
  • medical device 10 includes processing circuitry 350, memory 352, sensing circuitry 354 coupled to electrodes 356A and 356B (hereinafter, “electrodes 356”) and one or more sensor(s) 358, and communication circuitry 360.
  • Processing circuitry 350 may include fixed function circuitry and/or programmable processing circuitry.
  • Processing circuitry 350 may include any one or more of a microprocessor, a controller, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry.
  • processing circuitry 350 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more GPUs, one or more TPUs, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry.
  • memory 352 includes computer-readable instructions that, when executed by processing circuitry 350, cause IMD 10 and processing circuitry 350 to perform various functions attributed herein to IMD 10 and processing circuitry 350.
  • Memory 352 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random-access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media.
  • RAM random-access memory
  • ROM read-only memory
  • NVRAM non-volatile RAM
  • EEPROM electrically-erasable programmable ROM
  • flash memory or any other digital media.
  • Sensing circuitry 354 may monitor signals from electrodes 356 in order to, for example, monitor electrical activity of a heart of patient 4 and produce physiological data, e.g., cardiac data, such as ECG data or cardiac EGM data, for patient 4.
  • cardiac data such as ECG data or cardiac EGM data
  • processing circuitry 350 may identify features of the sensed cardiac signal, such as heart rate, heart rate variability, T-wave alternans, intra-beat intervals (e.g., QT intervals), and/or ECG morphologic features, to detect a cardiac episode and/or determine a glycemic state of patient 4.
  • Processing circuitry 350 may store the digitized cardiac signal and/or features of the cardiac signal used to detect the episode and/or to determine the glycemic state in memory 352 as cardiac data 388.
  • processing circuitry may store the digitized PPG signal and/or features of the PPG signal, e.g., pulse variability, frequency content, and/or frequency entropy, and/or may store the digitized impedance cardiography signal and/or features of the impedance cardiography signal, e.g., cardiac contractility indices, stroke volume, and/or ejection fraction.
  • medical device 10 includes one or more sensors 358, such as one or more accelerometers, gyroscopes, microphones, optical sensors, temperature sensors, pressure sensors, and/or chemical sensors.
  • sensing circuitry 354 may include one or more filters and amplifiers for filtering and amplifying signals received from one or more of electrodes 356 and/or sensors 358.
  • sensing circuitry 354 and/or processing circuitry 350 may include a rectifier, filter and/or amplifier, a sense amplifier, comparator, and/or analog-to-digital converter.
  • Processing circuitry 350 may determine cardiac data 388, e.g., values of physiological parameters of patient 4, based on signals from sensors 358, which may be stored in memory 352.
  • Patient parameters determined from signals from sensors 358 may include oxygen saturation, glucose level, stress hormone level, heart sounds, body motion, body posture, or blood pressure.
  • processing circuitry 350 may control communication circuitry 360 to transmit cardiac data 388, e.g., to external device 12 and/or computing system 8, for cloud-based or edge-based processing.
  • Communication circuitry 360 may include any suitable hardware, firmware, software, or any combination thereof for wirelessly communicating with another device, such as external device(s) 12.
  • Processing circuitry 350 may determine to control communication circuitry 360 to send a transmission based on cardiac data 388, e.g., one or more features of cardiac data 388, meeting one or more thresholds 390.
  • processing circuitry 350 may determine to capture a segment of cardiac data, e.g., 10 seconds of data or a specified number of samples, and control communication circuitry 360 to send the transmission of the cardiac data. In some examples, e.g., if communication circuitry 360 cannot establish a connection or cannot establish a sufficient connection, processing circuitry 350 determines the glycemic state of patient 4 based on the segment of cardiac signal data.
  • features e.g., QT interval changes, ST-segment deviations, R- wave duration to T-wave duration ratio changes R-wave amplitude to T-wave amplitude ratio changes, and/or T-wave abnormalities, e.g., T-wave inversion, bi-phasic T-waves, flat T-waves, T-waveretemans, meeting one or more corresponding feature thresholds of thresholds 390 for an amount of time meeting a time threshold of thresholds 390
  • processing circuitry 350 may determine to capture a segment of cardiac data, e.g., 10 seconds of data or
  • threshold(s) 390 may include feature thresholds.
  • One example of the feature threshold may be a QT interval threshold.
  • Processing circuitry 350 may analyze feature data of cardiac data 388, such as QT intervals, which is an example of feature data, of cardiac data 388 and determine QT intervals greater than the QT interval threshold. Stated another way, processing circuitry 350 may compare one or more features of the cardiac signal (e.g., QT intervals) to one or more corresponding feature thresholds (e.g., QT interval threshold).
  • the QT interval is a rate- corrected QT interval (QTc interval)
  • the QT interval threshold comprises a QTc threshold of, for example, 440 milliseconds (ms).
  • processing circuitry 350 may determine the QTc interval is indicative of hypoglycemia or hyperglycemia.
  • the QTc interval threshold may alternatively be a threshold change in QTc interval, e.g., a 7 ms change in QTc interval may be indicative of, e.g., hypoglycemia.
  • Threshold(s) 390 may also include a time threshold. Processing circuitry 350 may determine an amount of time the one or more features have met the one or more corresponding feature thresholds. For instance, processing circuitry 350 may determine an amount of time that the QT interval was greater than the QT interval threshold. Accordingly, processing circuitry 350 may compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold. [0069] Processing circuitry 350 may determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold.
  • processing circuitry 350 may determine whether there is a cardiac signal data segment, within the cardiac signals, where the cardiac signal data segment includes one or more features of the cardiac signal that satisfy the one or more corresponding feature thresholds for an amount of time that satisfies the time threshold. That is, processing circuitry 350 may determine whether there is a cardiac signal data segment, within the cardiac signals, where the cardiac signal data segment includes QT intervals of the cardiac signal that satisfy the QT interval threshold for an amount of time that satisfies the time threshold.
  • the QT interval was an example of features of the cardiac signal.
  • the features of the cardiac signal may be a running average of the QT interval duration.
  • Processing circuitry 350 may output the determined cardiac signal data segment and/or the features of the cardiac signal (e.g., to computing system 8). Computing system 8 may then determine a glycemic state of the patient based on the cardiac signal data segment and/or the features of the cardiac signal and output an indication indicative of the glycemic state of the patient. However, in some examples, such as if medical device 10 is an external medical device, similar to external device 12, processing circuitry 350 may determine a glycemic state of the patient based on the cardiac signal data segment and/or the features of the cardiac signal and output an indication indicative of the glycemic state of the patient.
  • threshold(s) 390 may include additional feature thresholds.
  • One example of the feature threshold may be a R-wave to T-wave ratio threshold.
  • the R-wave to T-wave ratio threshold is patient-specific and/or based on a placement of medical device 10.
  • Processing circuitry 350 may analyze feature data of cardiac data 388, such as R-wave to T-wave ratio, which is an example of feature data, of cardiac data 388 and determine R-wave to T-wave ratios greater than the R-wave to T-wave ratio threshold. Stated another way, processing circuitry 350 may compare one or more features of the cardiac signal (e.g., R-wave to T-wave ratio) to one or more corresponding feature thresholds (e.g., R-wave to T-wave ratio threshold).
  • the R-wave to T-wave ratio threshold may be specific to a patient and/or a placement of medical device 10. In some examples, the R-wave to T-wave ratio threshold may be based on patient 4’s baseline R-wave to T-wave ratio, e.g., the R- wave to T-wave ratio threshold may be twice the baseline R-wave to T-wave ratio. In some examples, e.g., in examples in which electrode placement is standardized, the R- wave to T-wave ratio, e.g., a R-wave amplitude to T-wave amplitude ratio may increase from 1 (e.g., during a normal glycemic state) to between 5 and 10 (e.g., during a hypoglycemic state).
  • the R-wave to T-wave ratio is an example of a relationship between an R- wave and a T-wave, such as a duration of an R-wave and a duration of a T-wave or an amplitude of an R-wave and an amplitude of a T-wave and should not be considered limiting.
  • There are other examples of a relationship between an R-wave and a T-wave such as a frequency of uniphasic, biphasic, and multiphasic R-waves and a frequency of uniphasic, biphasic, and multiphasic T-waves, that could be the one or more features of the cardiac signal used to determine a cardiac signal data segment.
  • Processing circuitry 350 may determine an amount of time the one or more features have met the one or more corresponding feature thresholds. For instance, processing circuitry 350 may determine an amount of time that the R-wave to T-wave ratio was greater than the R-wave to T-wave ratio threshold. Accordingly, processing circuitry 350 may compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold.
  • Processing circuitry 350 may determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold. For example, processing circuitry 350 may determine whether there is a cardiac signal data segment, within the cardiac signal, where the cardiac signal data segment includes one or more features of the cardiac signal that satisfy the one or more corresponding feature thresholds for an amount of time that satisfies the time threshold. That is, processing circuitry 350 may determine whether there is a cardiac signal data segment, within the cardiac signals, where the cardiac signal data segment includes R-wave to T-wave ratios of the cardiac signal that satisfy the R-wave to T-wave ratio threshold for an amount of time that satisfies the time threshold.
  • the R-wave to T-wave ratio was an example of features of the cardiac signal.
  • the features of the cardiac signal may be a running average of the R-wave to T- wave ratio across multiple instances of the polarization depolarization of the heart).
  • threshold(s) 390 may include additional feature thresholds, such as a T-wave abnormality threshold.
  • Processing circuitry 350 may determine whether the cardiac signal data includes T-wave abnormalities, such as T-wave inversion, bi-phasic T-waves, flat T-waves, or T-wave alternans, by comparing the cardiac signal data to the T-wave abnormality threshold.
  • processing circuitry 350 may additionally or alternative include an ST-segment deviation threshold. Processing circuitry 350 may compare the cardiac signal data to an ST-segment deviation threshold to identify deviations in the ST-segment.
  • Processing circuitry 350 may output the determined cardiac signal data segment and/or the features of the cardiac signal (e.g., to computing system 8). Computing system 8 may then determine a glycemic state of the patient based on the cardiac signal data segment and output an indication indicative of the glycemic state of the patient. However, in some examples, such as if medical device 10 is an external medical device, similar to external device 12, processing circuitry 350 may determine a glycemic state of the patient based on the cardiac signal data segment and/or the features of the cardiac signal and output an indication indicative of the glycemic state of the patient.
  • FIG. 4 is a block diagram illustrating an example configuration of computing system 8 of FIG. 1, in accordance with one or more techniques of this disclosure.
  • computing system 8 includes processing circuitry 402 for executing applications 424.
  • Computing system 8 may be any component or system that includes processing circuitry or other suitable computing environment for executing software instructions and, for example, need not necessarily include one or more elements shown in FIG. 4 (e.g., input devices 404, communication circuitry 406, user interface devices 410, or output devices 412; and in some examples components such as storage device(s) 408 or processing circuitry 402 may not be co-located or in the same chassis as other components).
  • computing system 8 may be a cloud computing system distributed across a plurality of devices.
  • computing system 8 includes processing circuitry 402, one or more input devices 404, communication circuitry 406, one or more storage device(s) 408, user interface (UI) device(s) 410, and one or more output devices 412.
  • Computing system 8 in some examples, further includes one or more application(s) 424, and operating system 416 that are executable by computing system 8.
  • Each of components 402, 404, 406, 408, 410, and 412 are coupled (physically, communicatively, and/or operatively) for inter-component communications.
  • communication channels 414 may include a system bus, a network connection, an interprocess communication data structure, or any other method for communicating data.
  • components 402, 404, 406, 408, 410, and 412 may be coupled by one or more communication channels 414.
  • Processing circuitry 402 in one example, is configured to implement functionality and/or process instructions for execution within computing system 8.
  • processing circuitry 402 may be capable of processing instructions stored in storage device(s) 408.
  • Examples of processing circuitry 402 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry.
  • One or more storage device(s) 408 may be configured to store information within computing system 8 during operation.
  • Storage device(s) 408, in some examples, is described as a computer-readable storage medium.
  • storage device(s) 408 is a temporary memory, meaning that a primary purpose of storage device(s) 408 is not long-term storage.
  • Storage device(s) 408, in some examples, is described as a volatile memory, meaning that storage device(s) 408 does not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.
  • RAM random access memories
  • DRAM dynamic random access memories
  • SRAM static random access memories
  • storage device(s) 408 is used to store program instructions for execution by processing circuitry 402.
  • Storage device(s) 408, in one example, is used by software or applications 424 running on computing system 8 to temporarily store information during program execution.
  • Storage device(s) 408 may be configured to store larger amounts of information than volatile memory.
  • Storage device(s) 408 may further be configured for long-term storage of information.
  • storage device(s) 408 include nonvolatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable memories (EEPROM).
  • Computing system 8 also includes communication circuitry 406 to communicate with other devices and systems, such as IMD 10 and external device 12 of FIG. 1, as well as other networked client external devices of various users.
  • Communication circuitry 406 may include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information.
  • network interfaces may include 3G and Wi-Fi radios.
  • Computing system 8 also includes one or more user interface devices 410.
  • User interface devices 410 are configured to receive input from a user through tactile, audio, or video feedback.
  • Examples of user interface devices(s) 410 include a presence-sensitive display, a mouse, a keyboard, a voice responsive system, video camera, microphone or any other type of device for detecting a command from the user.
  • a presence-sensitive display includes a touch- sensitive screen.
  • One or more output device(s) 412 may also be included in computing system 8.
  • Output device(s) 412 in some examples, is configured to provide output to the user using tactile, audio, or video stimuli.
  • Output device(s) 412 in one example, includes a presencesensitive display, a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines.
  • Additional examples of output device(s) 412 include a speaker, a liquid crystal display (LCD), or any other type of device that can generate intelligible output to the user.
  • Computing system 8 may include operating system 416.
  • Operating system 416 controls the operation of components of computing system 8.
  • operating system 416 in one example, facilitates the communication of one or more applications 424 with processing circuitry 402, communication circuitry 406, storage device(s) 408, input devices 404, user interface devices 410, and output devices 412.
  • Applications 424 may also include program instructions and/or data that are executable by computing system 8. Applications 424 may implement machine learning model(s) 452. Applications 424 may include other additional applications not shown which may alternatively or additionally be included to provide other functionality described herein and are not depicted for the sake of simplicity.
  • computing system 8 receives physiological signal data, e.g., cardiac signal data, e.g., from one or more medical devices, such as medical device 10 and/or external device 12, e.g., in examples in which external device 12 is a watch.
  • the cardiac signal data may be cardiac data 388.
  • Computing system 8 receives the cardiac signal data via communication circuitry 406, e.g., to determine the glycemic state of patient 4.
  • Processing circuitry 402 may store the cardiac signal data, e.g., cardiac data 388, in storage device(s) 408.
  • the cardiac data may have been collected by medical device 10 in response to device 10 detecting a sustained change in one or more features of the cardiac signals meeting a feature threshold for a threshold period of time.
  • Machine learning model(s) 452 controls the determination of glycemic state, and generation of reports for the user, e.g., for patient 4 and/or for clinician review.
  • Applications 424 may utilize input devices 404, output devices 412, and/or communication circuitry 406 to output glycemic state information to one or more users, e.g., via one or more graphical user interfaces presented on output devices 412 or other client external devices.
  • Processing circuitry 402 may determine one or more trends in glycemic state.
  • processing circuitry may implement machine learning model(s) 452 to determine the one or more trends.
  • processing circuitry 402 may determine a change in glycemic state over a period of time, e.g., weeks or months.
  • changes in glycemic state over a period of time may be indicative of a change in patient condition, e.g., an onset of diabetes.
  • Machine learning models 452 may include, as examples, neural networks, such as deep neural networks, which may include convolutional neural networks, multi-layer perceptrons, transformers, recurrent neural networks, and/or echo state networks, as examples.
  • neural networks such as deep neural networks, which may include convolutional neural networks, multi-layer perceptrons, transformers, recurrent neural networks, and/or echo state networks, as examples.
  • FIG. 5 is a flow diagram illustrating an example operation for determining a glycemic state of a patient, in accordance with one or more techniques of this disclosure.
  • Sensing circuitry e.g., sensing circuitry 354 of FIG. 3 or sensing circuitry of external device 12, senses a physiological signal, e.g., a cardiac signal (502).
  • processing circuitry 350 may store the cardiac signal as cardiac data 388.
  • Processing circuitry of system 2 e.g., processing circuitry 402 of computing system 8, processing circuitry 350 of medical device 10, or processing circuitry of external device 12, compares one or more features of the cardiac signal to one or more corresponding feature thresholds (504).
  • the one or more features of the cardiac signal may include one or more of a QT-interval duration, e.g., an average QT-interval duration, a QTc duration, or a change in QTc, or an R-wave to T- wave ratio, e.g., an R-wave duration to T-wave duration ratio, e.g., an average R-wave duration to T-wave duration ratio, or an R-wave amplitude to T-wave amplitude ratio.
  • the one or more feature thresholds may comprise a threshold range with an upper limit and a lower limit. In some examples, one or more of the one or more feature thresholds may comprise a single value, e.g., an upper limit or a lower limit.
  • processing circuitry of system 2 determines the one or more feature thresholds. In some examples, a clinician may determine the one or more feature thresholds. In some examples, processing circuitry of system 2 presents, e.g., via user interface device(s) 410, the one or more proposed feature thresholds to the clinician and prompts the clinician to accept or modify the one or more feature thresholds.
  • Processing circuitry of system 2 compares an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold between 5 minutes and 24 hours, e.g., 30 minutes or 60 minutes (506). In some examples, processing circuitry of system 2 determines the time threshold value. In some examples, the clinician may determine the time threshold value. In some examples, processing circuitry of system 2 presents a proposed time threshold value to the clinician and prompts the clinician to accept or modify the time threshold value. Processing circuitry of system 2 may determine whether the amount of time the one or more features have met the one or more corresponding feature thresholds meets the time threshold.
  • the time threshold is a first time threshold
  • processing circuitry of system 2 additionally determines one or more immediately subsequent thresholds, e.g., the first time threshold plus an additional time of, e.g., 10 minutes.
  • the one or more feature thresholds and/or the time threshold may be patient-specific.
  • processing circuitry of system 2 may determine the one or more feature thresholds and/or the time threshold based on historical patient data, e.g., EHR data.
  • processing circuitry of system 2 may implement a machine learning algorithm to determine the one or more feature thresholds and/or the time threshold.
  • processing circuitry of system 2 may initially output the determined cardiac signal segment after the amount of time meets the first threshold of, e.g., 30 minutes. If the amount of time meets an immediately subsequent threshold, e.g., an additional 10 minutes for a total of 40 minutes, processing circuitry of system 2 may determine and output the additional cardiac signal segment.
  • processing circuitry 350 of medical device 10 may control communication circuitry 360 to output the determined cardiac signal data segment.
  • processing circuitry of external device 12 may control communication circuitry of external device 12 to output the determined cardiac signal data segment.
  • one or more of processing circuitry 350, processing circuitry 402, or processing circuitry of external device 12 receives the determined cardiac signal data segment (512). In some examples, one or more of processing circuitry 350, processing circuitry 402, or processing circuitry of external device 12 additionally receives the features of the cardiac signal data. In some examples, the same processing circuitry of system 2 that outputs the determined cardiac signal data segment receives the cardiac signal data segment. For instance, if processing circuitry 350 of medical device 10 controls communication circuitry 360 to output the determined cardiac signal data segment, processing circuitry 350 may, in some examples, receive the cardiac signal data segment.
  • processing circuitry of external device 12 may, in some examples, receive the cardiac signal data segment
  • one of communication circuitry 360, communication circuitry 406, or communication circuitry of external device 12 may output the cardiac signal data segment to one or more of the remaining communication circuitries of system 2.
  • communication circuitry 360 outputs the determined cardiac signal data segment
  • processing circuitry 402 or processing circuitry of external device 12 may receive the determined cardiac signal data segment.
  • Processing circuitry of system 2 determines the glycemic state of patient 4 (514). Processing circuitry of system 2 may determine the glycemic state of patient 4 at least based on the determined cardiac signal data segment. In some examples, processing circuitry of system 2 may additionally determine the glycemic state of patient 4 based on the features of the cardiac signal, EHR data, and/or other physiological signals of patient 4. Processing circuitry of system 2 controls communication circuitry of system 2 to output an indication indicative of the glycemic state of the patient (516). In some examples, communication circuitry of system 2, e.g., communication circuitry 406, may output the indication via user interface device(s) 410 and/or a user interface of external device 12. The indication may comprise an indication of whether the current glycemic state is actionable, e.g., whether the glycemic state is indicative of hyperglycemia or hypoglycemia.
  • FIG. 6 is a flow diagram illustrating an example operation for periodically determining a glycemic state of a patient, in accordance with one or more techniques of this disclosure.
  • processing circuitry of system 2 e.g., processing circuitry 402 of computing system 8
  • processing circuitry 350 of medical device 10, or processing circuitry of external device 12 may determine glycemic state based on periodically, e.g., hourly, daily, or weekly, captured segments of cardiac signal data.
  • processing circuitry 350 may determine the glycemic state.
  • processing circuitry 350 of medical device 10 may control communication circuitry 360 of medical device 10 to transmit cardiac signal data to, e.g., external device 12 or computing system 8, to determine the glycemic state, e.g., to conserve battery life (602).
  • a periodic schedule of the cardiac signal data transmissions may be patient-specific and/or processing circuitry of system 2 may adjust the periodic schedule based on changes in patient disease state.
  • Processing circuitry of system 2, e.g., processing circuitry 402 of computing system 8 determines the glycemic state of patient 4 based on the transmitted cardiac signal data (604).
  • processing circuitry 402 determines the glycemic state by implementing a machine learning model.
  • Processing circuitry 402 controls user interface(s) 410 to output an indication not the user indicative of the glycemic state of patient 4 based on the transmitted cardiac signal data (606).
  • FIG. 7 is a flow diagram illustrating an example operation for adjusting one or more monitoring and/or transmission parameters based on a risk of diabetes, in accordance with one or more techniques of this disclosure.
  • patient 4 may have diabetes or may be at risk of becoming diabetic, e.g., patients with heart failure or other cardiac conditions may be at increased risk of becoming diabetic.
  • Patient 4 may additionally be at risk of other comorbid conditions, such as kidney failure or cardiorenal metabolic disease.
  • the techniques of this disclosure may facilitate monitoring of comorbid patient conditions.
  • processing circuitry of system 2 determines a risk of diabetes for patient 4 (702).
  • processing circuitry of system 2 implements a machine learning model to determine the risk of diabetes for patient 4.
  • the machine learning model may be trained based on historical patient data and/or aggregate patient data.
  • Processing circuitry of system 2 controls, e.g., user interface device(s) 410, to output an indication of the risk of diabetes (704).
  • the indication of the risk may include a risk score.
  • the indication may include a risk category, e.g., low, moderate, or high.
  • processing circuitry of system 2 controls user interface device(s) 410 to provide an indication to patient 4 and/or the clinician that patient 4’s risk category has changed.
  • processing circuitry of system 2 controls user interface device(s) 410 to output urgent notifications to patient 4 and/or the clinician, e.g., when processing circuitry of system 2 determines patient 4 is experiencing an relatively serious current actionable event and/or when the features of the cardiac signal are indicative of substantial changes.
  • FIG. 8 is a flow diagram illustrating an example operation for adjusting one or more monitoring and/or transmission parameters based on user input, in accordance with one or more techniques of this disclosure.
  • Processing circuitry of system 2 e.g., processing circuitry 402 of computing system 8, processing circuitry 350 of medical device 10, or processing circuitry of external device 12, may receive user input, e.g., via user interface device(s) 410 of computing system 8 (802).
  • processing circuitry 402 may prompt the clinician to provide user input after outputting the glycemic state information.
  • Processing circuitry 402 may update one or more of the one or more corresponding feature thresholds, the time threshold, or the periodic schedule, e.g., a frequency of periodic signal data transmissions, based on the user input (804).
  • processing circuitry 402 may determine a recommended adjustment to the one or more of the one or more corresponding feature thresholds, the time threshold, or the frequency of periodic signal data transmissions based on glycemic state information.
  • the screen of user interface device(s) 410 may output the recommended adjustment for clinician approval.
  • the clinician may input adjustments independently of the glycemic state information. For example, the clinician may increase the frequency of the periodical signal data transmissions for a patient who is anxious and would like to receive glycemic state information more frequently.
  • FIG. 9 is a conceptual diagram illustrating an example machine learning model 900 configured to determine a glycemic state of a patient, in accordance with one or more techniques of this disclosure.
  • Machine learning model 900 is an example of a set of rules, e.g., a set of rules implemented by machine learning models 452 of computing system 8 in wireless communication with medical device 10.
  • Machine learning model 900 is an example of a deep learning model, or deep learning algorithm, trained to determine a glycemic state of patient 4 based on physiological data, e.g., cardiac data 388.
  • One or more of medical device 10, external device 12, or a computing system 8 may train, store, and/or utilize machine learning model 900, but other devices may apply inputs associated with a particular patient to machine learning model 900 in other examples.
  • machine learning and deep learning models or algorithms may be utilized in other examples.
  • a convolutional neural network model of ResNet-18 may be used.
  • models that may be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, DenseNet, transformer models such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), etc.
  • Some non-limiting examples of machine learning techniques include Support Vector Machines, K-Nearest Neighbor algorithm, and Multi-layer Perceptron.
  • machine learning model 900 may include three layers. These three layers include input layer 902, hidden layers 904, and output layer 906. Output layer 906 comprises the output from the transfer function 905 of output layer 906. Input layer 902 represents each of the input values XI through X4 provided to machine learning model 900. The number of inputs may be less than or greater than 4, including much greater than 4, e.g., hundreds or thousands. In some examples, the input values may any of the of values input into a machine learning model, as described above. In some examples, input values may include samples of a cardiac signal. In addition, in some examples input values of machine learning model 900 may include additional data, such as data relating to one or more additional parameters of patient 4.
  • Each of the input values for each node in the input layer 902 is provided to each node of hidden layer 904.
  • hidden layers 904 include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples.
  • Each input from input layer 902 is multiplied by a weight and then summed at each node of hidden layers 904.
  • the weights for each input are adjusted to establish the relationship between the inputs, e.g., cardiac data 388, to determine glycemic state of patient 4.
  • one hidden layer may be incorporated into machine learning model 900, or three or more hidden layers may be incorporated into machine learning model 900, where each layer includes the same or different number of nodes.
  • the result of each node within hidden layers 904 is applied to the transfer function of output layer 906.
  • the transfer function may be linear or non-linear, and in some examples may depend on the number of layers within machine learning model 900.
  • Example non-linear transfer functions may be a sigmoid function or a rectifier function.
  • the output 907 of the transfer function may be a classification that indicates whether the particular cardiac segment or other input set represents hyperglycemia, hypoglycemia, or a normal glycemic state.
  • processing circuitry By applying the cardiac signal data and/or other patient parameter data to a machine learning model, such as machine learning model 900, processing circuitry, such as processing circuitry 402 of computing system 8, is able to determine a glycemic state of patient 4.
  • model 900 is but one example, and other examples may be utilized in implementing the techniques of this disclosure.
  • a machine learning model may include different types of layers, such as pooling layers.
  • a machine learning model may include features not illustrated in the example of FIG. 9, such as skip connections and weight sharing.
  • FIG. 10 is a conceptual diagram illustrating an example training process for a machine learning model 1000 being trained using supervised and/or reinforcement learning techniques, in accordance with examples of the current disclosure.
  • Machine learning model 1000 may be substantially similar to or the same as machine learning model 900 (FIG. 9).
  • Machine learning model 1000 may be implemented using any number of models for supervised and/or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, a naive Bayes network, a support vector machine, or a k-nearest neighbor model. In other examples, machine learning model 1000 may be implemented using any number of models for unsupervised learning.
  • processing circuitry one or more of medical device 10, external device 12, and/or computing system 8 initially trains the machine learning model 1000 based on training set data 1002 including numerous instances of input data corresponding to a plurality of glycemic states, e.g., as labeled by an expert.
  • a prediction or classification by the machine learning model 1000 may be compared 1006 to the target output 1004, e.g., as determined based on the label.
  • the processing circuitry implementing a learning/training function 1008 may send or apply a modification to weights of machine learning model 1000 or otherwise modify/update the machine learning model 1000.
  • one or more of medical device 10, external device 12, and/or computing system 8 may, for each training instance in the training set 1002, modify machine learning model 1000 to change a glycemic state determination generated by the machine learning model 1000 in response to data applied to the machine learning model 1000.
  • a system comprising a medical device, the medical device comprising: sensing circuitry configured to sense a cardiac signal of a patient; and processing circuitry configured to: compare one or more features of the cardiac signal to one or more corresponding feature thresholds; compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold; determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and output the determined cardiac signal data segment.
  • Example 2 The system of Example 1, further comprising a computing system configured to: receive the determined cardiac signal data segment; determine a glycemic state of the patient based on the cardiac signal data segment; and output an indication indicative of the glycemic state of the patient.
  • Example 3 The system of any of Examples 1 and 2, wherein the processing circuitry is further configured to: store a plurality of data segments of the cardiac signal; and periodically transmit the plurality of data segments of the cardiac signal.
  • Example 4 The system of any one or more of Examples 2 and 3, wherein the computing system is further configured to: based on the glycemic state of the patient, output a risk of diabetes for the patient.
  • Example 5 The system of Example 4, wherein the processing circuitry is further configured to: adjust one or more of the one or more feature thresholds, the time threshold, or a frequency of periodic signal data transmissions.
  • Example 6 The system of any one or more of Examples 1-5, wherein the determination of the glycemic state is cloud-based.
  • Example 7 The system of any one or more of Examples 1-6, wherein the one or more features comprise one or more of: a QT interval duration; or a relationship between an R-wave and a T-wave.
  • Example s The system of Example 7, wherein to compare the one or more features of the cardiac signal to the one or more corresponding thresholds, the processing circuitry is configured to compare one or more of: a running average of the QT interval duration to a QT interval threshold of the one or more corresponding thresholds; or a running average of the relationship between the R-wave and the T-wave to a R-wave to T-wave threshold of the one or more corresponding thresholds.
  • Example 9 The system of any one or more of Examples 1-8, wherein the time threshold is between 5 minutes to 24 hours.
  • Example 10 The system of any one or more of Examples 1-9, wherein the processing circuitry is further configured to: based on user input, update one or more of the one or more corresponding thresholds or the time threshold.
  • Example 11 The system of any of Examples 1 and 7-10, wherein the processing circuitry is configured to: determine a glycemic state of the patient based on the cardiac signal data segment; and output an indication indicative of the glycemic state of the patient.
  • Example 12 A method comprising: sensing, by sensing circuitry of a medical device of a system, a cardiac signal of a patient; comparing, by processing circuitry of the medical device, one or more features of the cardiac signal to one or more corresponding thresholds; comparing, by the processing circuitry, an amount of time the one or more features have met the one or more corresponding thresholds to a time threshold; determining, by the processing circuitry, a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and outputting, by the processing circuitry, the determined cardiac signal data segment.
  • Example 13 The method of Example 12, wherein the system further comprises a computing system, further comprising: receiving, by the processing circuitry, the determined cardiac signal data segment; determining, by the processing circuitry, a glycemic state of the patient based on the cardiac signal data segment; and outputting, by the processing circuitry, an indication indicative of the glycemic state of the patient.
  • Example 14 The method of any of Examples 11 and 12, further comprising: storing, by the processing circuitry, a plurality of data segments of the cardiac signal; and periodically transmitting, by the processing circuitry, the plurality of data segments of the cardiac signal.
  • Example 15 The method of any one or more of Examples 12-14, further comprising: based on the glycemic state of the patient, outputting, by the computing system, a risk of diabetes for the patient.
  • Example 16 The method of Example 15, further comprising: adjusting, by the processing circuitry, one or more of the one or more feature thresholds, the time threshold, or a frequency of periodic signal data transmissions.
  • Example 17 The method of any one or more of Examples 12-16, wherein the determination of the glycemic state is cloud-based.
  • Example 18 The method of any one or more of Examples 12-17, wherein the one or more features comprise one or more of: a QT interval duration; or a relationship between an R-wave and a T-wave.
  • Example 19 The method of Example 18, wherein comparing the one or more features of the cardiac signal to the one or more corresponding thresholds comprises one or more of: comparing a running average of the QT interval duration to a QT interval threshold of the one or more corresponding thresholds; or comparing a running average of the relationship between the R-wave and the T-wave to a R-wave to T-wave threshold of the one or more corresponding thresholds.
  • Example 20 The method of any one or more of Examples 12-19, wherein the time threshold is between 5 minutes to 24 hours.
  • Example 21 The method of any one or more of Examples 12-20, further comprising: based on user input, updating, by the processing circuitry, one or more of the one or more corresponding thresholds or the time threshold.
  • Example 22 The method of any of Examples 12 and 18-21, further comprising: determining, by the processing circuitry, a glycemic state of the patient based on the cardiac signal data segment; and output an indication indicative of the glycemic state of the patient.
  • Example 23 A non-transitory computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to: compare one or more features of the cardiac signal to one or more corresponding feature thresholds; compare an amount of time the one or more features have met the one or more corresponding feature thresholds to a time threshold; determine a cardiac signal data segment from the cardiac signal based on the comparison of the amount of time the one or more features have met the one or more corresponding feature thresholds to the time threshold; and output the determined cardiac signal data segment.
  • Various examples have been described. These and other examples are within the scope of the following claims.

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Abstract

Un système de l'invention comprend un dispositif médical, le dispositif médical comprenant un circuit de détection configuré pour détecter un signal cardiaque d'un patient, et un circuit de traitement configuré pour : comparer une ou plusieurs caractéristiques du signal cardiaque à un ou plusieurs seuils de caractéristique correspondants ; comparer une durée pendant laquelle la ou les caractéristiques ont satisfait le ou les seuils de caractéristique correspondants à un seuil de temps ; déterminer un segment de données de signal cardiaque à partir du signal cardiaque sur la base de la comparaison de la durée pendant laquelle la ou les caractéristiques ont satisfait le ou les seuils de caractéristique correspondants au seuil de temps ; et délivrer le segment de données de signal cardiaque déterminé.
PCT/IB2025/054281 2024-04-24 2025-04-24 État glycémique courant basé sur un signal cardiaque Pending WO2025224669A1 (fr)

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US7769436B1 (en) * 2007-06-04 2010-08-03 Pacesetter, Inc. System and method for adaptively adjusting cardiac ischemia detection thresholds and other detection thresholds used by an implantable medical device
US20120184867A1 (en) * 2007-10-30 2012-07-19 Taraneh Ghaffari Farazi Systems and methods for increased specificity in diagnostics
US20180098704A1 (en) * 2011-04-29 2018-04-12 Medtronic, Inc. Method and device to monitor patients with kidney disease
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WO2022265841A1 (fr) * 2021-06-15 2022-12-22 Medtronic, Inc. Conditions de contournement d'algorithme de décision
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