WO2025153901A1

WO2025153901A1 - System configured to identify anomalies in regularly scheduled medical device electrocardiogram transmissions

Info

Publication number: WO2025153901A1
Application number: PCT/IB2025/050046
Authority: WO
Inventors: Veronica RAMOS
Original assignee: Medtronic Inc
Current assignee: Medtronic Inc
Priority date: 2024-01-18
Filing date: 2025-01-02
Publication date: 2025-07-24
Anticipated expiration: 2026-07-18

Abstract

Techniques are described for detecting abnormal waveforms and/or signal quality issues in regularly scheduled ECG transmissions. As an example, a computing system comprises processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

Description

SYSTEM CONFIGURED TO IDENTIFY ANOMALIES IN REGULARLY SCHEDULED MEDICAL DEVICE ELECTROCARDIOGRAM TRANSMISSIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63/622,208, filed January 18, 2024, and U.S. Provisional Patent Application Serial No. 63/632,684, filed April 11, 2024, the entire content of each is incorporated herein by reference.

FIELD

[0002] This disclosure generally relates to medical devices and, more particularly, to electrocardiogram monitoring devices.

BACKGROUND

[0003] A variety of implantable medical devices (IMD) have been clinically implanted or proposed for therapeutically treating or monitoring cardiac, neurological, and/or other conditions of a patient. IMDs may monitor physiological signals of the patient. For example, an IMD may monitor an electrocardiogram (ECG) of the patient to monitor, and in some cases treat arrhythmia conditions. In general, using these signals, such devices facilitate monitoring and evaluating patient health over a number of months or years, outside of a clinic setting.

[0004] In some cases, such devices are configured to detect acute health events based on the ECG, such as episodes of cardiac tachyarrhythmias. Some IMDs are capable of delivering therapy, e.g., electrical stimulation, to treat acute health events. For example, an IMD may deliver pacing pulses to the heart upon detecting tachycardia or bradycardia or may deliver cardioversion or defibrillation shocks to the heart upon detecting tachycardia or fibrillation. In some examples, features of the ECG may be oversensed, undersensed, or otherwise inappropriately sensed, which may interfere with proper detection and/or treatment of abnormal rhythms.

SUMMARY

[0005] In general, this disclosure describes techniques for monitoring patients by identifying abnormal waveforms in electrocardiograms (ECGs). More specifically, this disclosure describes techniques for identifying abnormal waveforms in presenting rhythm, nightly transmission, or other ECG data collected by implantable medical devices (IMDs) of a system and not associated with an arrhythmia episode. As used herein, abnormal waveforms may include physiologically abnormal waveforms, as well as waveforms with abnormal morphologies and/or waveforms resulting in misinterpretation by the ECG sensing medical device, which may be indicative of signal quality and/or analysis issues. [0006] As an example of a physiologically abnormal waveform, premature ventricular complexes (PVCs) may be present in an ECG. PVCs are premature depolarizations arising from an ectopic focus within the ventricles, occurring when a focus in the ventricle creates a potential before the next scheduled sinoatrial nodal action potential. The occurrence of PVCs is indicative of increased risk of SCA and may additionally be linked to mortality associated with myocardial infarction. PVCs can present in ECG signals as large peaks. As an example of an abnormal waveform indicative of a signal quality issue, oversensed T- waves may be present in an ECG and may be incorrectly detected as R-waves. T-wave oversensing can cause incorrect tachyarrhythmia detection and, in some cases, unnecessary therapy delivery. Additionally, baseline wandering in ECG signals can cause inaccurate R-wave detection, which can cause inappropriate detection of bradycardia, pause, or other arrhythmias and, in some cases, can cause unnecessary therapy delivery or result in withheld therapy. Other examples of indicators of signal quality issues may include baseline noise, signal to noise ratio (SNR), P-wave presence and/or morphology, R-wave presence, R-wave amplitude, and/or features indicative of potential contact loss. [0007] In some examples, implantable medical devices (IMDs) monitor ECG signals for acute health events via R-wave detection. The IMD may detect R-waves by identifying local maxima in the signal corresponding to R-wave peaks. In some examples, PVCs and/or oversensed or overpronounced T- waves may be misidentified as normal R-waves. In some examples, non-episodic, regularly scheduled transmissions may facilitate PVC monitoring and may identify T-wave oversensing and/or other indicators of signal quality and/or analysis issues.

[0008] In some examples, the ECG to be included in the regularly scheduled transmission of ECG data is collected at a specified time, e.g., 3 a.m. In other examples, the ECG for the regularly scheduled transmission is collected when patient activity levels are low, e.g., indicating the patient is sleeping or resting. In general, the ECG for such regularly scheduled transmissions is collected when patient movement and other physiological parameters are expected to be low/steady to provide a clean and typical ECG for user evaluation.

[0009] Although some medical devices that sense ECGs are themselves configured to detect abnormal waveforms, some of which may be indicative of signal quality issues within the ECG, the techniques of this disclosure may be implemented via a cloud computing system or another computing device that may be configured to implement more effective and resource intensive analysis of the ECG to identify the abnormal waveforms. Since the ECGs are collected regularly, e.g., daily, the techniques may allow regular analysis to identify abnormal waveforms more rapidly, and to identify changes in the incidence of abnormal waveforms over time. The techniques may include presentation of trends of abnormal waveforms over time to a clinician or other use.

[0010] The techniques may allow identification of abnormal waveforms that are negatively impacting the ECG sensing and/or arrhythmia detection fidelity of the medical device, allowing a user to better understand the true health condition of the patient and/or allowing adjustment of the medical device to remediate the abnormal waveforms. In this manner, the techniques may prevent abnormal waveforms, e.g., oversensed or overpronounced T-waves, in the ECG signal from being misidentified as normal R-waves, which may prevent misidentification of tachyarrhythmia and, in some cases, delivery of unnecessary therapy, e.g., shocks. Additionally, the techniques may prevent baseline wandering from impacting arrhythmia detection and, in some cases, treatment. The techniques may provide an indication of PVC burden, which may indicate the health of the patient, e.g., of the cardiac substrate of the patient.

[0011] For example, an IMD may, at regularly scheduled intervals, e.g., 3 a.m. daily, sense an ECG. In some examples, the IMD may additionally comprise an accelerometer to verify low patient activity before collecting the ECG for the regularly scheduled transmission. Processing circuitry of a system including the IMD, e.g., of a cloud computing system, may receive the regularly scheduled ECG transmission and may then use the ECG signal data to determine whether abnormal waveforms, e.g., PVCs or oversensed or overpronounced T-waves, are present. The processing circuitry may determine an abnormal waveform metric. In the example of the abnormal waveform comprising a PVC, the processing circuitry may process the metrics to determine if the patient is at an increased risk of a health event, e.g., SCA, based on the PVC metric. A notification of a risk of based on the PVC metric may be sent to the patient and/or a clinician, e.g., in the event of the assessment indicating the patient is at an increased risk of SCA.

[0012] In some examples, the processing circuitry may identify/quantify abnormal waveforms including oversensed or overpronounced T-waves, baseline wandering, and/or other indicators of signal quality issues and may output an indication to a user, e.g., the patient, the clinician, or a caretaker. The output may comprise an indication that the placement or configuration (e.g., programming) of the device should be adjusted based on the occurrence of T-wave oversensing or baseline wandering. By identifying and adjusting the IMD based on T-wave oversensing or baseline wandering in the ECG signal, the system may improve signal quality and/or analysis quality. Additionally, adjusting the device in response to T-wave oversensing or baseline wandering may improve PVC identification and thereby improve accuracy of SCA risk determinations.

[0013] In some examples, identifying T-wave oversensing, baseline wandering, or other indicators of signal quality issues in the ECG transmission may directly improve SCA risk identification. For example, PVCs and the associated risk of SCA may be identified by determining QT interval timing. By identifying and addressing T-wave oversensing in the ECG signal, the QT interval timing determination may become more accurate, which may facilitate appropriate therapy administration.

[0014] The techniques of this disclosure may further comprise updating the periodic schedule for regularly scheduled ECG transmissions in response to changes to the frequency or quantity of false positive cardiac event detections.

[0015] In an example, a computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determine an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, present an indication to a user that the ECG includes abnormal waveforms.

[0016] In another example, a method for operating a computing system, the method comprising: receiving, by processing circuitry of the computing system, a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determining, by the processing circuitry, one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determining, by the processing circuitry, an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, presenting, by the processing circuitry, an indication to a user that the ECG includes abnormal waveforms.

[0017] In another example, a non-transitory computer-readable storage medium comprising instructions that, when executed, cause processing circuitry of a computing system to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determine an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, present an indication to a user that the ECG includes abnormal waveforms.

[0018] In another example, a computing system comprises processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

[0019] In another example, a method comprises: receiving, by processing circuitry of a computing system, a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determining, by the processing circuitry, one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determining, by the processing circuitry, a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, presenting, by the processing circuitry, an indication to a user related to the determination that the ECG includes signal quality issues.

[0020] In another example, a non-transitory computer-readable medium stores instructions that when executed by processing circuitry cause the processing circuitry to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

[0021] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the apparatus and methods described in detail within the accompanying drawings and description below. Further details of one or more examples are set forth in the accompanying drawings and the description below.

BRIEF DESCRIPTION OF DRAWINGS

[0022] 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. [0023] FIG. 2 is a block diagram illustrating an example system configured to determine risk of SCA of a patient and/or identify signal quality issues in accordance with one or more techniques of this disclosure.

[0024] FIGS. 3 A and 3B are conceptual diagrams illustrating example implantable medical devices that operate in accordance with one or more techniques of this disclosure. [0025] FIG. 4 is a block diagram illustrating an example configuration of an implantable medical device that operates in accordance with one or more techniques of this disclosure.

[0026] FIG. 5 is a block diagram illustrating an example configuration of a computing system that operates in accordance with one or more techniques of this disclosure. [0027] FIG. 6 is a flow diagram illustrating an example operation for determining whether to inform a user that the ECG includes signal quality issues, in accordance with one or more techniques of this disclosure.

[0028] FIG. 7 is a graph illustrating an example ECG comprising PVCs, which may be identified in accordance with one or more techniques of this disclosure.

[0029] FIG. 8 is a flow diagram illustrating an example operation for determining whether to inform a user that the ECG includes abnormal waveforms, in accordance with one or more techniques of this disclosure.

[0030] FIG. 9 is a flow diagram illustrating an example operation for determining a risk of SCA for a patient, in accordance with one or more techniques of this disclosure.

[0031] FIG. 10 is a graph illustrating an example ECG comprising a potential PVC, which may be identified in accordance with one or more techniques of this disclosure.

[0032] FIG. 11 is a graph illustrating an example ECG comprising oversensed T- waves, which may be identified in accordance with one or more techniques of this disclosure.

[0033] FIG. 12 is a flow diagram illustrating an example operation for determining whether to adjust a configuration or placement of a medical device in response to T-wave oversensing, in accordance with one or more techniques of this disclosure.

[0034] FIG. 13 is a graph illustrating an example ECG comprising a potential oversensed T-wave, which may be identified in accordance with one or more techniques of this disclosure.

[0035] FIG. 14 is a flow diagram illustrating an example operation for determining whether to adjust a configuration or placement of a medical device in response to baseline wandering, in accordance with one or more techniques of this disclosure.

[0036] FIG. 15 is a graph illustrating an example ECG comprising potential baseline wandering, which may be identified in accordance with one or more techniques of this disclosure.

[0037] FIG. 16 is a flow diagram illustrating an example operation for determining whether to update a periodic schedule for ECG transmission, in accordance with one or more techniques of this disclosure. [0038] FIG. 17 is a flow diagram illustrating an example operation for identifying a change in signal quality issues over time, in accordance with one or more techniques of this disclosure.

[0039] Like reference characters refer to like elements throughout the figures and description.

DETAILED DESCRIPTION

[0040] A variety of types of implantable and external devices are configured to monitor health based on sensed electrocardiograms (ECGs) and, in some cases, other physiological signals. External devices that may be used to non-invasively sense and monitor ECGs and other physiological signals include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, 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.

[0041] Implantable medical devices (IMDs) also sense and monitor ECGs 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 LINQ™ or LINQ II™ 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, to a remote patient monitoring system, such as the Medtronic CareLink™ Network via a home monitoring system or a smart phone application.

[0042] In some examples, IMDs of a system may additionally collect regularly scheduled transmissions based on a periodic schedule to identify abnormal waveforms, which may be indicative of signal quality and/or analysis issues or underlying patient health issues. For example, the system may identify premature ventricular complexes (PVCs) as normal R- waves or may identify T- waves that the IMD misidentified as R- waves. Additionally, the system may identify baseline wandering present in the signal, which may result in inaccurate R-wave detection. Other examples of indicators of signal quality and/or analysis issues include baseline noise, R-wave presence, R-wave amplitude, features indicative of potential contact loss, signal to noise ratio (SNR), and P-wave presence and/or morphology. In examples in which the IMD is configured to deliver therapy, signal quality and/or analysis issues may cause unnecessary therapy administration, e.g., shocks.

[0043] Additionally, the presence of PVCs in an ECG signal can be indicative of an increased risk of sudden cardiac arrest (SCA) in patients. SCA can cause sudden cardiac death (SCD) and is associated with a high mortality rate.

[0044] The techniques of this disclosure may provide one or more technical and clinical advantages. For example, the techniques of this disclosure may be implemented by a system including an IMD that can continuously (e.g., on a periodic basis without human intervention) sense signals while subcutaneously implanted in a patient over months or years to enable the systems herein to determine the presence of abnormal waveforms and/or other indicators of signal quality and/or analysis issues and enable a user to respond to such waveforms. A PVC metric determined based on regularly scheduled ECG transmissions may advantageously enable a clinical determination of whether a patient has an increased risk of SCA. By continuously monitoring the patient and generating an alert if the patient has an increased risk of SCA, a user, e.g., the patient, the clinician, or a caretaker, may identify the risk of SCA early and allow the user to take preventive measures, which may lead to better patient outcomes.

[0045] As another example, the techniques of this disclosure may be advantageous in that in addition to identifying abnormal waveforms and/or other indicators of signal quality and/or analysis issues in episodic data, the techniques of this disclosure comprise identifying abnormal waveforms and/or other indicators of signal quality and/or analysis issues in non-episodic data. Non-episodic data may comprise data closer to a patient baseline relative to episodic data, and in some cases, may comprise less noise due to patient activity. The techniques of this disclosure may therefore result in more accurate abnormal waveform identification and/or identification of other indicators of signal quality and/or analysis issues. [0046] Additionally, in some examples, abnormal waveforms, e.g., oversensed or overpronounced T-waves, may initially be misclassified as normal R-waves, which may cause inaccurate R-wave quantification and/or morphology analysis. By identifying abnormal waveforms and/or other indicators of signal quality issues in non-episodic ECG transmissions, the techniques of this disclosure may allow a user to understand potential issues with previous episode detection by the IMD and to potentially remediate the oversensing or other issues. In some examples, PVCs and the associated risk of SCA may be further identified by determining QT interval timing. By identifying and addressing T- wave oversensing in the ECG signal, the QT interval timing determination may be more accurate.

[0047] The system may additionally be configured to update the periodic schedule for regularly scheduled ECG transmissions in response to changes to the frequency or quantity of false positive cardiac event detections.

[0048] In some examples, the techniques of this disclosure may include determining trends in abnormal waveforms and/or other indicators of signal quality and/or analysis issues over time. By determining trends in abnormal waveforms and/or other indicators of signal quality issues over time, the techniques of this disclosure may advantageously allow a user to understand signal quality issues to potentially remediate the signal quality issues for the particular patient and/or medical device, or for a class of patients and/or medical devices.

[0049] FIG. 1 illustrates the environment of an example medical device system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. The example techniques may be used with one or more patient sensing devices, e.g., including an IMD 10, which may be in wireless communication with one or more computing devices, e.g., external device 12. System 2 additionally comprises a network 220 and a computing system 230. External device 12 and computing system 230 are interconnected and may communicate with each other through network 220. Although not illustrated in FIG. 1, IMD 10 includes electrodes and/or other sensors to sense an ECG signal of patient 4 and may collect and store ECG data based on the sensed ECG signal. In some examples, IMD 10 additionally includes one or more sensors, e.g., an accelerometer, to determine patient activity level. One or more elements of system 2 may assess a risk of SCA for patient 4 and/or may assess signal quality issues based on the collected ECG data. [0050] IMD 10 may be implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 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, and be configured to sense an ECG and/or other physiological signals from that position. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II™ ICM.

[0051] Although described primarily in the context of examples in which IMD 10 takes the form of an ICM, the techniques of this disclosure may be implemented in systems including any one or more implantable or external medical devices, including monitors, pacemakers, defibrillators (e.g., subcutaneous or substemal), wearable external defibrillators (WAEDs), neurostimulators, drug pumps, patch monitors, or wearable physiological monitors, e.g., wrist or head wearable devices. Examples with multiple IMDs or other sensing devices may be able to collect different data useable by system 2 to determine a risk of SCA of patient 4 and/or determine whether signal quality issues are present in the ECG.

[0052] External device 12 may be a computing device with a display viewable by the user and an interface for providing input to external device 12 (i.e., a user input mechanism). External device 12 is configured for wireless communication with IMD 10. External device 12 retrieves sensed physiological data from IMD 10 that was collected and stored by the IMD. In some examples, external device 12 takes the form of a personal computing device of patient 4. For example, external device 12 may take the form of a smartphone of patient 4. In some examples, external device 12 may be any computing device configured for wireless communication with IMD 10, such as a desktop, laptop, or tablet computer. External device 12 may communicate with IMD 10 via near-field communication technologies e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm, and far- field communication technologies, e.g., radiofrequency telemetry according to the Bluetooth® or Bluetooth® Low Energy (BLE) protocols, or other communication technologies operable at ranges greater than near-field communication technologies. When external device 12 is configured for use by the clinician, external device 12 may be used to transmit instructions to IMD 10. The clinician may also configure and store operational parameters for IMD 10 with the aid of external device 12. In some examples, external device 12 assists the clinician in the configuration of IMD 10 by providing a system for identifying potentially beneficial operational parameter values.

[0053] External device 12 may be used to retrieve data from IMD 10. The retrieved data may include ECG data measured by IMD 10 based on ECG signals sensed by IMD 10. For example, external device 12 may retrieve ECG data on a regular transmission schedule, e.g., 3 a.m. daily. In some examples, instead or in addition to ECG signal data, external device may retrieve ECG data indicating values of metrics determined by IMD 10 based on the ECG signal. The ECG data transmission may comprise ECG data over a specified duration of time, e.g., 10 seconds, and/or comprise a specified number of ECG data samples.

[0054] Processing circuitry of system 2, e.g., of IMD 10, external device 12, computing system 230, and/or one or more other computing devices (not illustrated in FIG. 1) may be configured to perform the example techniques described herein for identifying abnormal waveforms and/or other indicators of signal quality and/or analysis issues and, in some examples, determining risk of SCA based on ECG data collected by IMD 10. In some examples, one or more of the sensors, e.g., of IMD 10, may be implanted within patient 4, that is, implanted at least subcutaneously. In some examples, one or more of the sensors of IMD 10 may be located externally to patient 4, for example as part of a cuff or as a wearable device.

[0055] In some examples, IMD 10 transmits data to an external device 12 e.g., a smartphone of patient 4, which may then transmit the data to computing system 230 via network 220. Additionally, or alternatively, IMD 10 may transmit data to computing system 230 via an access point (not illustrated in FIG. 1). Computing system 230 may be configured to process the data and notify the clinician and/or the patient when the ECG signal comprises abnormal waveforms and/or other indicators of signal quality issues. In some examples, computing system 230 may additionally be configured to notify the clinician and/or the patient when the patient has an increased risk of SCA.

[0056] FIG. 2 is a block diagram illustrating an example system that includes an access point 210, a network 220, external computing devices, such as computing system 230, and one or more other computing devices 240A-240N, which may be coupled to IMD 10, and external device 12 via network 220, in accordance with one or more techniques described herein. IMD 10 may communicate with external device 12 via a first wireless connection and may communicate with an access point 210 via a second wireless connection. In the example of FIG. 2, access point 220, external device 12, computing system 230, and computing devices 240A-240N are interconnected and may communicate with each other through network 220.

[0057] Access point 210 may include a device that connects to network 220 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 210 may be coupled to network 220 through different forms of connections, including wired or wireless connections. In some examples, access point 90 may be a user device, such as a tablet or smartphone, that may be co-located with the patient. As discussed above, IMD 10 may be configured to transmit physiological data, e.g., ECG data, accelerometer data, to external device 12. In addition, access point 210 may interrogate IMD 10, such as periodically or in response to a command from the patient or network 220, in order to retrieve patient data from IMD 10. Access point 210 may communicate the retrieved data to computing system 230 via network 220.

[0058] In some cases, computing system 230 may be configured to provide a secure storage site for data that has been collected from IMD 10, and/or external device 12. In some cases, computing system 230 may assemble data for viewing by clinicians via computing devices 240A-240N. One or more aspects of the illustrated system of FIG. 2 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink™ Network developed by Medtronic, Inc.

[0059] Computing system 230 may include processing circuitry 234. Processing circuitry 234 may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry 234 may include any one or more of a microprocessor, a controller, digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), graphics processing unit (GPU), tensor processing unit (TPU), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 234 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FGPAs, one or more GPUs, one or more TPUs, as well as other discrete of integrated logic circuitry. The functions attributed to processing circuitry 234 may be embodied as software, firmware, hardware, or any combination thereof. In some examples, processing circuitry 234 may perform one or more techniques described herein to identify abnormal waveforms and/or other indicators of signal quality issues, and in some cases a risk of SCA, based on an ECG signal received from IMD 10.

[0060] Computing system 230 may include storage device 232. Storage device 232 includes computer-readable instructions that, when executed by processing circuitry 234, cause IMD 10 and processing circuitry 234 to perform various functions attributed to IMD 10 and processing circuitry 234 herein. Storage device 232 may include any volatile, nonvolatile, magnetic, optical, or electrical media, such as random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electronically erasable programmable ROM (EEPROM), flash memory, or any other digital media.

[0061] In some examples, one or more of computing devices 240A-240N (collectively, “computing devices 240”), e.g., computing device 240A, may be a tablet or other smart device located with the clinician, by which the clinician may program, receive alerts from, and/or interrogate IMD 10. For example, the clinician may access data corresponding to an ECG signal collected by IMD 10, or metric values determined by IMD 10 based on the ECG signal, through device 240A and computing system 230, such as when patient 4 is in between clinician visits, to check on signal quality and/or analysis quality of the IMD, e.g., check for presence of oversensed or overpronounced T-waves, baseline wandering, other indicators of signal quality and/or analysis issues, and/or to check on a status of a medical condition, e.g., presence of PVCs and/or a risk of SCA. In some examples, the clinician may enter instructions for medical intervention for patient 4 into an application in computing device 240A, such as based on a status of a patient condition determined by IMD 10, computing system 230, or the combination thereof, or based on other patient data known to the clinician. Computing device 240A may then transmit the instructions for medical intervention to another of computing devices 240, e.g., computing device 240B, located with patient 4 or a caregiver of patient 4. For example, such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, or to seek medical attention. In this manner, patient 4 may be empowered to take action, as needed, to address his or her medical status, which may help improve clinical outcomes for patient 4. [0062] FIG. 3A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIG. 1 as an ICM. In the example shown in FIG. 3A, IMD 10A may be embodied as a monitoring device having housing 312, proximal electrode 316A and distal electrode 316B. Housing 312 may further comprise first major surface 314, second major surface 318, proximal end 320, and distal end 322. Housing 312 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Housing 312 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 316A and 316B.

[0063] In the example shown in FIG. 3A, IMD 10A is defined by a length L, 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. In one example, 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. For example, the device shown in FIG. 3A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, the spacing between proximal electrode 316A and distal electrode 316B 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. In addition, 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. In addition, the width W of major surface 314 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. In addition, IMD 10A according to an example of the present disclosure is 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. [0064] In the example shown in FIG. 3A, once inserted within the patient, the first major surface 314 faces outward, toward the skin of the patient while the second major surface 318 is located opposite the first major surface 314. In addition, in the example shown in FIG. 3A, proximal end 320 and distal end 322 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 Publication No. 2014/0276928, incorporated herein by reference in its entirety.

[0065] Proximal electrode 316A is at or proximate to proximal end 320, and distal electrode 16B is at or proximate to distal end 322. Proximal electrode 316A and distal electrode 316B are used to sense ECG signals thoracically outside the ribcage, which may be implanted sub-muscularly or subcutaneously. ECG signals may be stored in a memory of IMD 10A, and data may be transmitted via integrated antenna 33OA to another device, which may be another implantable device or an external device, such as external device 312. In some examples, electrodes 316A and 316B 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.

[0066] In the example shown in FIG. 3 A, proximal electrode 316A is at or in close proximity to the proximal end 320 and distal electrode 316B is at or in close proximity to distal end 322. In this example, distal electrode 316B is not limited to a flattened, outward facing surface, but may extend from first major surface 314 around rounded edges 324 and/or end surface 326 and onto the second major surface 318 so that the electrode 316B has a three-dimensional curved configuration. In some examples, electrode 316B is an uninsulated portion of a metallic, e.g., titanium, part of housing 312.

[0067] In the example shown in FIG. 3 A, proximal electrode 316A is located on first major surface 314 and is substantially flat, and outward facing. However, in other examples proximal electrode 316A may utilize the three-dimensional curved configuration of distal electrode 316B, providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 316B may utilize a substantially flat, outward facing electrode located on first major surface 314 similar to that shown with respect to proximal electrode 316A. [0068] The various electrode configurations allow for configurations in which proximal electrode 316A and distal electrode 316B are located on both first major surface 314 and second major surface 318. In other configurations, such as that shown in FIG. 3 A, only one of proximal electrode 316A and distal electrode 316B is located on both major surfaces 314 and 318, and in still other configurations both proximal electrode 316A and distal electrode 316B are located on one of the first major surface 314 or the second major surface 318 (e.g., proximal electrode 316A is located on first major surface 314 while distal electrode 316B is located on second major surface 318). In another example, IMD 10A may include electrodes on both major surface 314 and 318 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A. Electrodes 316A and 316B 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.

[0069] In the example shown in FIG. 3A, proximal end 320 includes a header assembly 328 that includes one or more of proximal electrode 316A, integrated antenna 33OA, anti-migration projections 332, and/or suture hole 334. Integrated antenna 33OA is located on the same major surface (i.e., first major surface 314) as proximal electrode 316A and is also included as part of header assembly 328. Integrated antenna 33OA allows IMD 10A to transmit and/or receive data. In other examples, integrated antenna 33OA may be formed on the opposite major surface as proximal electrode 316A or may be incorporated within the housing 312 of IMD 10A. In the example shown in FIG. 3A, antimigration projections 332 are located adjacent to integrated antenna 33OA and protrude away from first major surface 314 to prevent longitudinal movement of the device. In the example shown in FIG. 3A, anti-migration projections 332 include a plurality of small bumps or protrusions (e.g., nine small bumps) extending away from first major surface 314. As discussed above, in other examples anti-migration projections 332 may be located on the opposite major surface as proximal electrode 316A and/or integrated antenna 33OA. In addition, in the example shown in FIG. 3A, header assembly 328 includes suture hole 334, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 334 is located adjacent to proximal electrode 316A. In one example, header assembly 328 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.

[0070] FIG. 3B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIG. 1 as an ICM. IMD 10B of FIG. 3B may be configured substantially similarly to IMD 10A of FIG. 3A, with differences between them discussed herein.

[0071] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g., an ICM. IMD 10B includes housing having a base 340 and an insulative cover 342. Proximal electrode 316C and distal electrode 316D may be formed or placed on an outer surface of cover 342. Various circuitries and components of IMD 10B may be formed or placed on an inner surface of cover 342, or within base 340. In some examples, a battery or other power source of IMD 10B may be included within base 340. In the illustrated example, antenna 33OB is formed or placed on the outer surface of cover 342 but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 342 may be positioned over an open base 340 such that base 340 and cover 342 enclose the circuitries and other components and protect them from fluids such as body fluids. The housing including base 370 and insulative cover 372 may be hermetically sealed and configured for subcutaneous implantation.

[0072] Circuitries and components may be formed on the inner side of insulative cover 342, such as by using flip-chip technology. Insulative cover 342 may be flipped onto a base 340. When flipped and placed onto base 340, the components of IMD 10B formed on the inner side of insulative cover 342 may be positioned in a gap 344 defined by base 340. Electrodes 316C and 316D and antenna 33OB may be electrically connected to circuitry formed on the inner side of insulative cover 342 through one or more vias (not shown) formed through insulative cover 342. Insulative cover 342 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Base 340 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 316C and 316D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 316C and 316D 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. [0073] In the example shown in FIG. 3B, the housing of IMD 10B defines a length L, 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. 3A. For example, the spacing between proximal electrode 316C and distal electrode 316D 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. In addition, IMD 10B may have a length L that ranges from 5 mm to about 70 mm. In other examples, 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. In addition, the width W 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.

[0074] In the example shown in FIG. 3B, once inserted subcutaneously within the patient, outer surface of cover 342 faces outward, toward the skin of the patient. In addition, as shown in FIG. 3B, proximal end 346 and distal end 348 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. In addition, edges of IMD 10B may be rounded.

[0075] FIG. 4 is a block diagram illustrating an example configuration of an IMD 10 in accordance with one or more techniques described herein. IMD 10 may correspond to either of IMDs 10A and 10B, or another configuration of an IMD. In the illustrated example, IMD 10 includes electrodes 316 (which may correspond to any of electrodes 316A-316D), processing circuitry 450, sensing circuitry 454, sensors 458, communication circuitry 460, power source 482, and memory 452. Although the illustrated example includes two electrodes 316, IMDs including or coupled to more than two electrodes may implement the techniques of this disclosure in some examples. Power source 482 provides operational power for processing circuitry 450, sensing circuitry 454, sensors 458, communication circuitry 460, and memory 452. [0076] Processing circuitry 450 may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry 450 may include any one or more of a microprocessor, a controller, a DSP, an ASIC, a FPGA, a GPU, a TPU, or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 450 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, one or more GPUs, one or more TPUs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 450 herein may be embodied as software, firmware, hardware or any combination thereof.

[0077] Sensing circuitry 454 may be coupled to electrodes 316 to sense electrical signals of the heart of patient 4, for example by selecting electrodes 316 and polarity, used to sense an ECG as controlled by processing circuitry 450. Sensing circuitry 454 may sense the ECG from electrodes 316 in order to facilitate monitoring the electrical activity of the heart. In some examples, sensing circuitry 454 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 316 and/or sensors 458. Sensing circuitry 454 and processing circuitry 450 may store ECG data 454 in memory 452. Sensing circuitry 454 may also monitor signals from sensors 458, which may include one or more accelerometers, other vibration or motion sensors, optical sensors, or BP sensors, as examples. Sensing circuitry 454 may capture sensor signals from any one of sensors 458, e.g., to produce other patient data, in order to facilitate monitoring of patient activity.

[0078] Communication circuitry 460 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 450, communication circuitry 460 may receive downlink telemetry from, as well as send uplink telemetry to external device 12. In addition, processing circuitry 450 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLink™ Network. Communication circuitry 460 may be configured to transmit and/or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes. [0079] Memory 452 may be configured to store information within IMD 10 during operation. Memory 452, in some examples, is described as a computer-readable storage medium. In some examples, memory 452 is a temporary memory, meaning that a primary purpose of memory 452 is not long-term storage. Memory 452, in some examples, is described as a volatile memory, meaning that memory 452 does not maintain stored contents when the computer is turned off. Examples of volatile memories include RAM, dynamic RAM (DRAM), static RAM (SRAM), and other forms of volatile memories known in the art. In some examples, memory 452 is used to store program instructions for execution by processing circuitry 450. Memory 452, in one example, is used by software or applications 470 running on IMD 10A to temporarily store information during program execution.

[0080] Memory 452, in some examples, also includes one or more non-transitory computer-readable storage media. Memory 452 may be configured to store larger amounts of information than volatile memory. Memory 452 may further be configured for longterm storage of information. In some examples, memory 452 includes non-volatile 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 EEPROM memories.

[0081] In some examples, processing circuitry 450 may process ECG data 454 to detect abnormal waveforms and/or other indicators of signal quality issues, and optionally to determine patient risk of SCA. In some examples, processing circuitry 450 may control communication circuitry 460 to transmit ECG data or metric data to another device, e.g., from external device 12 to computing system 230 or computing devices 240A-240N, for analysis to detect abnormal waveforms and/or other indicators of signal quality issues according to the techniques of this disclosure.

[0082] Processing circuitry 450 may also use activity signal information, e.g., determined via an accelerometer signal, to determine whether collection of the ECG signal for the regularly scheduled transmission should take place. In some examples, sensors 458 may be configured to monitor patient activity. The system may collect ECG data when the patient is at a low activity level, e.g., when the patient is asleep, to minimize effects of patient activity on the ECG signal. In some examples, IMD 10 may be triggered to sense signals based on a desired activity state and/or posture. For example, the system may be configured to collect the regularly scheduled transmission ECG data at any time that the patient has a low activity level. In other examples, the system may attempt to collect the regularly scheduled ECG data transmission at a particular time, e.g., 3 am, at which the patient is expected to be asleep, lying down, or otherwise inactive. Sensor 458 may be configured to confirm the patient is inactive before transmitting the ECG signal. In some examples, sensor 458 comprises an accelerometer.

[0083] FIG. 5 is a block diagram illustrating an example configuration of components of computing system 230. In the example of FIG. 5, computing system 230 includes user interface 504, storage device 232, processing circuitry 234, and communication circuitry 506.

[0084] Processing circuitry 234 may include one or more processors that are configured to implement functionality and/or process instructions for execution within computing system 230. For example, processing circuitry 234 may be capable of processing instructions stored in storage device 232. Processing circuitry 234 may include, for example, microprocessors, DSPs, ASICs, FPGAs, GPUs, TPUs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 234 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 234.

[0085] Communication circuitry 506 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12. Under the control of processing circuitry 234, communication circuitry 506 may receive downlink telemetry from, as well as send uplink telemetry to, external device 12, IMD 10, or another device. Communication circuitry 506 may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, NFC, RF communication, Bluetooth, Wi-Fi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 506 may also be configured to communicate with devices other than external device 12 or IMD 10 via any of a variety of forms of wired and/or wireless communication and/or network protocols.

[0086] Storage device 232 may be configured to store information within computing system 230 during operation. Storage device 232 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 232 includes one or more of a short-term memory or a long-term memory. Storage device 232 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 232 is used to store data indicative of instructions for execution by processing circuitry 234. Storage device 232 may be used by software or applications running on computing system 230 to temporarily store information during program execution.

[0087] Computing system 230 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters/device settings and/or export collected data. For example, processing circuitry 234 may transmit an instruction to IMD 10 which requests IMD 10 to export collected data (e.g., ECG data 454) to computing system 230, via external device 12 and/or access point 220. In turn, computing system 230 may receive the collected data from IMD 10 via external device 12 and/or access point 220 and store the collected data in storage device 232, e.g., as ECG data 518. The data computing system 230 receives from IMD 10 may include various patient data such ECG signal, activity signal, and/or metric values derived from these signals, and any other patient data. Processing circuitry 234 may implement any of the techniques described herein to analyze the data from IMD 10 for abnormal waveforms and/or other indicators of signal quality and/or analysis issues and, in some cases, indicia of risk of SCA of patient 4.

[0088] Processing circuitry 514 of computing system 230, using information contained in storage device 232, e.g., signal quality issue metrics 520, threshold(s) 516, and ECG data 518, is operative to analyze data of patient 4 collected by IMD 10 to determine whether the ECG comprises abnormal waveforms and/or other indicators of signal quality and/or analysis issues and whether the patient is at increased risk of SCA. Although described in the context of computing system 230, the techniques described herein may similarly be performed by any computing device, such as external device 12, or by IMD 10.

[0089] Based on ECG data 518, processing circuitry 234 determines one or more values of PVC metrics 510, T-wave oversensing metrics 512, baseline metrics 514, R- wave metrics 522, P-wave metrics 524, SNR metrics 5226, and/or potential contact loss metrics 528 of signal quality issue metrics 520, based on ECG signal collected by electrodes 316. [0090] In some examples, processing circuitry 234 determines a total signal quality issue metric based on a quantification of the one or more values of PVC metrics 510, T- wave oversensing metrics 512, baseline metrics 514, R-wave metrics 522, P-wave metrics 524, SNR metrics 5226, and/or potential contact loss metrics 528. To determine the one or more values based on the ECG signal, processing circuitry 234 may perform an analysis of the ECG signal.

[0091] To determine PVC metrics 510, processing circuitry may identify one or more waveforms in the ECG signal indicative of PVCs, e.g., relatively wide spikes in the ECG signal. Processing circuitry 234 may determine PVC metrics 510 based on a quantification of PVCs in the signal. PVCs in the ECG signal may be indicative of an increased risk of SCA and/or other conditions, such as increased risk of death due to myocardial infarction. By identifying PVCs in the signal and determining PVC metrics 510, the techniques of this disclosure may facilitate identification of potential patient conditions.

[0092] Baseline metrics 514 may include one or more of baseline wandering metrics or baseline noise metrics. Processing circuitry 234 may apply one or more filters, such as filters configured for QRS suppression, to the ECG signal to identify baseline wandering and/or baseline noise in the ECG signal. Processing circuitry 234 may determine baseline metrics 514 based on a quantification of baseline wandering and/or baseline noise.

Baseline wandering and baseline noise can be indicative of signal quality and/or analysis issues. In some examples, baseline wandering can be caused by motion artifacts and/or poor electrode contact. Baseline noise can be caused by electrical interference. Baseline wandering and/or baseline noise in the ECG signal can lead to signal analysis issues, which can cause inaccurate arrhythmia detections and, in some examples, unnecessary therapy delivery. By determining baseline metrics 514, the techniques of this disclosure may facilitate detection of baseline wandering and/or baseline noise issues, which may improve the accuracy of arrhythmia detections.

[0093] To determine T-wave oversensing metrics 512, processing circuitry 234 may identify one or more T-waves in the signal and may determine T-wave oversensing metrics 512 based on a quantification of the one or more T-waves. T-wave oversensing can be caused by device placement issues and/or by patient factors such as a relatively high body mass index (BMI). T-waves erroneously classified as R-waves can lead to inaccurate arrhythmia detections and, in some examples, unnecessary therapy delivery. By determining T-wave metrics 512, the techniques of this disclosure may improve the accuracy of arrhythmia detections.

[0094] R-wave metrics 522 may include, as examples, R-wave amplitude metrics and/or R-wave presence metrics. Processing circuitry 234 may determine an average, maximum, minimum, or other statistical characterization of the amplitudes of R-waves in the ECG signal as part of determining R-wave amplitude metrics. Processing circuitry 234 may determine whether R-waves are present in the ECG signal and/or whether an expected number of R-waves are present in the signal. Processing circuitry 234 may determine R-wave presence metrics based on a quantification of the R-waves present or absent in the signal. In some examples, the R-wave presence metrics may be a binary value, e.g., 1 for R-waves present in signal and 0 for no R-waves present in signal.

Processing circuitry 234 may determine R-wave metrics 522 based on a quantification of R-wave amplitude metrics and/or R-wave presence metrics. Relatively low R-wave amplitudes and/or missing R-waves in the ECG signal can be caused by, for example, device placement, device sensitivity, and/or patient factors such as BMI. Relatively low R- wave amplitudes and/or missing R-waves due to signal quality issues can lead to inaccurate arrhythmia detections and other issues, which can in some cases lead to unnecessary therapy delivery. By determining R-wave metrics 522, the techniques of this disclosure may improve the accuracy of arrhythmia detections.

[0095] Processing circuitry 234 may determine P-wave metrics 524 based on one or more of P-wave presence metrics or P-wave morphology metrics. Processing circuitry 234 may determine whether P-waves are present in the ECG signal and/or whether an expected number of P-waves are present in the signal. Processing circuitry 234 may determine P- wave presence metrics based on a quantification of the P-waves present or absent in the signal. In some examples, the P-wave presence metrics may be a binary value, e.g., 1 for P-waves present in signal and 0 for no P-waves present in signal. Processing circuitry may determine P-wave morphology metrics by identifying morphologic abnormalities, e.g., inverted P-waves, and/or changes relative to a baseline morphology in the P-wave.

Processing circuitry 234 may determine P-wave metrics 524 based on a quantification of P-wave presence metrics and/or P-wave morphology metrics. In some examples, processing circuitry 234 may determine whether patient 4 is experiencing atrial fibrillation (AF) based on whether P-waves are present in the signal. In some examples, if P-waves are absent from the signal, processing circuitry may determine patient 4 is experiencing AF. Relatively low P-wave amplitudes and/or missing P-waves in the ECG signal can also be caused by, for example, device placement, device sensitivity, and/or patient factors, such as BMI, and can lead to inaccurate AF or other arrhythmia detections, which can in some cases lead to unnecessary therapy delivery. By determining P-wave metrics 524, the techniques of this disclosure may facilitate signal quality issue detection, allowing a user and/or processing circuitry 234 to remediate the signal quality issues, thereby improving the accuracy of arrhythmia detections.

[0096] Processing circuitry 234 may additionally determine SNR metrics 526 based on a SNR of the ECG signal. In some examples, SNR metrics 526 is a quantification of the SNR of the ECG signal. A relatively high SNR can be caused by, for example, placement issues. A relatively high SNR can lead to inaccurate arrhythmia detections and, in some examples, unnecessary therapy delivery. By determining SNR metrics 526, the techniques of this disclosure may facilitate SNR-related signal quality issues, which may allow a user and/or processing circuitry 234 to remediate the SNR-related signal quality issues, improving the accuracy of arrhythmia detections.

[0097] To determine potential contact loss metrics 528, processing circuitry 234 may determine a morphology and/or features of the ECG signal are indicative of a loss or instability of electrode contact to tissue of patient 4. Processing circuitry may determine potential contact loss metrics 528 based on a quantification of the morphology and/or features of the ECG indicative of the loss or instability of the electrode contact to tissue of patient 4. By determining potential contact loss metrics 528, the techniques of this disclosure may allow a user to remediate the associated signal quality issues, which can improve the accuracy of arrhythmia detections.

[0098] PVC metrics 510, T-wave oversensing metrics 512, and baseline metrics 514, R-wave metrics 522, P-wave metrics 524, SNR metrics 5226, and potential contact loss metrics 528 are specific examples of abnormal waveform metrics and/or other indicators of signal quality and/or analysis issue metrics. Processing circuitry 234 compares signal quality issue metrics 520 to respective thresholds 516. Processing circuitry 234 then determines whether the ECG includes abnormal waveforms and/or other indicators of signal quality and/or analysis issues. In the example of the abnormal waveforms comprising PVCs, processing circuitry 234 may also determine whether the patient has an increased risk of SCA. Processing circuitry 234 may additionally determine, based on detection of one or more of, as examples, T-wave oversensing or baseline wandering, whether the user should adjust the placement or the configuration of the IMD. In some examples, based on detections of signal quality and/or analysis issues in the ECG, processing circuitry 234 may additionally determine to flag the change in configuration and/or apply the change in configuration to one or more other devices, e.g., to IMDs of patients other than patient 4 with the same model of implant, to remediate signal quality and/or analysis issues associated with the one or more other devices.

[0099] The user, such as the clinician or patient 4, may interact with computing system 230 through user interface 504. User interface 504 includes a display (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry 234 may present information related to IMD 10, e.g., evidence of abnormal waveforms and/or other indicators of signal quality and/or analysis issues, a risk indication for SCA, and visualizations of various data such as cardiac ECG. In addition, user interface 504 may include an input mechanism configured to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry 234 of computing system 230 and provide input. In other examples, user interface 504 also includes audio circuitry for providing audible notifications, instructions, or other sounds to the user, receiving voice commands from the user, or both.

[00100] FIG. 6 is a flow diagram illustrating an example operation for determining whether to inform a user that the ECG includes signal quality and/or analysis issues. FIG.

6 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 6 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A-240N.

[0100] According to the example of FIG. 6, computing system 230 receives an ECG transmission from IMD 10 that was collected by IMD 10 on a periodic schedule (602). Processing circuitry 234 of computing system 230 determines whether one or more abnormal waveforms and/or other indicators of signal quality issues, e.g., based on oversensed or overpronounced T waves, PVCs, baseline wandering, baseline noise, R- wave presence, R-wave amplitude, P-wave presence and/or morphology, SNR, and/or potential contact loss, are present in the ECG transmission (604). If there are no abnormal waveforms and/or other indicators of signal quality issues present in the transmission (“NO” of 604), the operation ends. If there are abnormal waveforms and/or other indicators of signal quality issues present in the transmission (“YES” of 604), processing circuitry 234 determines a signal quality issue metric (606). The signal quality issue metric may comprise a quantification of the number of abnormal waveforms and/or other indicators of signal quality issues in the transmission and/or a quantification of one or more of PVC metrics 510, baseline metrics 514, T-wave oversensing metrics 512, R-wave metrics 522, P-wave metrics 524, SNR metrics 526, or potential contact loss metrics 528 of computing system 230. The signal quality issue metric may additionally be based on an assessment of the morphology or one or more features of the abnormal waveforms and/or other indicators of signal quality issues. The signal quality issue metric may additionally be based on a confidence level associated with the identification of the abnormal waveform and/or other indicators of signal quality issues. For example, if processing circuitry 234 is relatively confident, e.g., 95% confident, the signal quality issue metric may be higher than if processing circuitry 234 is less confident, e.g., 90% confident. Processing circuitry 234 compares the signal quality issue metric to a threshold (608). [0101] If the signal quality issue metric does not meet the threshold (“NO” of 608), the operation ends. If the signal quality issue metric meets the threshold (“YES” of 608), processing circuitry 234 causes communication circuitry 506 to present an indication to the user regarding the determination that the ECG includes abnormal waveforms and/or other indicators of signal quality and/or analysis issues that the IMD should be adjusted, or, in some examples, e.g., in examples in which the abnormal waveforms comprise PVCs, that the patient has an increased risk of SCA (610). In some examples, communication circuitry 506 communicates with one or more of communication circuitry of external device 12 or computing devices 240A-240N to present the indication to the user.

[0102] FIG. 7 is a graph illustrating an example ECG comprising PVCs, which may be identified in accordance with one or more techniques of this disclosure. FIG. 7 comprises an example transmission of a predetermined period of time, e.g., 10 seconds. In other examples, the predetermined period of time may be longer, e.g., 30 seconds. The transmission may additionally or alternatively comprise a predetermined number of samples. ECG transmission signal 706 comprises three peaks corresponding to PVCs 702 and five peaks corresponding to normally conducted R- waves 704. In some examples, processing circuitry of the system, e.g., processing circuitry 234 of computing system 230, may initially identify R-waves by identifying local maxima within the ECG transmission. In some examples, IMD 10 may have identified R-waves when collecting the ECG transmission and provided indications of the locations of R-waves within the ECG data. In such examples, processing circuitry 234 of computing system may identify local maxima within respective windows around the R-wave locations identified by IMD 10. During analysis, the system may differentiate between R-waves and PVCs.

[0103] FIG. 8 is a flow diagram illustrating an example operation for determining whether to inform a user that the ECG includes abnormal waveforms, in accordance with one or more techniques of this disclosure. FIG. 8 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 8 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A- 240N. FIG. 8 may comprise a specific example of FIG. 6 wherein the one or more abnormal waveforms include one or more of PVCs or oversensed or overpronounced T- waves. FIG. 8 may alternatively comprise an operation responsive to the operation of FIG. 6, i.e., upon detection of abnormal waveforms and/or other indicators of signal quality and/or analysis issues, processing circuitry 234 may initiate the operation described herein. [0104] According to the example of FIG. 8, computing system 230 receives an ECG transmission from IMD 10 that was collected by IMD 10 on a periodic schedule (802). Processing circuitry 234 of computing system 230 determines whether one or more abnormal waveforms, e.g., oversensed or overpronounced T waves, PVCs, or baseline wandering, are present in the ECG transmission (804). In some examples, to determine whether one or more abnormal waveforms are present in the ECG, processing circuitry 234 detects a plurality of local maxima within the ECG associated with a plurality of R- waves identified by IMD 10, and for each of the local maxima, processing circuitry determines whether a curve associated with the local maximum is an abnormal waveform and not a normal R-wave. If there are no abnormal waveforms present in the transmission (“NO” of 806), the operation ends. If there are abnormal waveforms present in the transmission (“YES” of 806), processing circuitry 234 determines an abnormal waveform metric (808). The abnormal waveform metric may be a specific example of signal quality issues metric 520 of computing system 230. The abnormal waveform metric may comprise a quantification of the number of abnormal waveforms in the transmission. The abnormal waveform metric may additionally be based on an assessment of the morphology of the abnormal waveforms. The abnormal waveform metric may additionally be based on a confidence level associated with the abnormal waveform identification. For example, if processing circuitry 234 is relatively confident, e.g., 95% confident, the abnormal waveform metric may be higher than if processing circuitry 234 is less confident, e.g., 90%. Processing circuitry 234 compares the abnormal waveform metric to a threshold (810).

[0105] In the example of the abnormal waveform comprising a PVC, the threshold may comprise a PVC threshold. In the example of the abnormal waveform comprising a T-wave, the threshold may comprise a T-wave oversensing threshold. In some examples, processing circuitry 234 may differentiate between abnormal waveforms corresponding to PVCs and abnormal waveforms corresponding to T-wave oversensing based on the PVC threshold and the T-wave oversensing threshold. The PVC threshold and the T-wave oversensing threshold may be different from one another, which can facilitate differentiation between abnormal waveforms corresponding to PVCs and abnormal waveforms corresponding to T-wave oversensing. In some examples, the PVC threshold and the T-wave oversensing threshold may comprise respective threshold windows.

[0106] If the abnormal waveform metric does not meet the threshold (“NO” of 810), the operation ends. If the abnormal waveform metric meets the threshold (“YES” of 810), processing circuitry 234 causes communication circuitry 506 to present an indication to the user regarding the determination that the ECG includes abnormal waveforms, e.g., that the ECG includes abnormal waveforms, that the IMD should be adjusted, or that the patient has an increased risk of SCA (812). In some examples, communication circuitry 506 communicates with one or more of communication circuitry of external device 12 or computing devices 240A-240N to present the indication to the user.

[0107] FIG. 9 is a flow diagram illustrating an example operation for determining a risk of SCA for a patient and whether to provide an indication to the user of elevated SCA risk. FIG. 9may comprise a specific example of FIG. 6 or FIG. 8, wherein the one or more abnormal waveforms and/or other indicators of signal quality and/or analysis issues include one or more PVCs. FIG. 9 may alternatively comprise an operation responsive to the operation of FIG. 6 or FIG. 8, i.e., upon detection of abnormal waveforms, processing circuitry may initiate the operation described herein. FIG. 9 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 9 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A-240N.

[0108] According to the example of FIG. 9, computing system 230 receives an ECG transmission from IMD 10 that was collected by IMD 10 on a periodic schedule (902). Processing circuitry 234 of computing system 230 determines whether one or more PVCs are present in the ECG transmission (904). In some examples, to determine whether one or more PVCs are present in the ECG, processing circuitry 234 detects a plurality of local maxima within the ECG associated with a plurality of R-waves identified by IMD 10, and for each of the local maxima, processing circuitry determines whether a curve associated with the local maximum is a PVC and not a normal R-wave. If there are no PVCs present in the transmission (“NO” of 906), the SCA risk determination operation ends. If there are PVCs present in the transmission (“YES” of 906), processing circuitry 234 determines a PVC metric (908). The PVC metric may comprise a quantification of the number of PVCs in the transmission. The PVC metric may also be based on an assessment of the morphology of the PVCs, e.g., isolated PVCs, consecutive PVCs. For example, processing circuitry 234 may determine a different PVC metric for a transmission with two separated PVCs and a transmission with two consecutive PVCs. The PVC metric may additionally be based on a confidence level associated with the PVC identification. For example, if processing circuitry 234 is relatively confident, e.g., 95% confident, the PVC metric may be higher than if processing circuitry 234 is less confident, e.g., 90%. Processing circuitry 234 compares the PVC metric to a PVC threshold (910). If the PVC metric does not meet the PVC threshold (“NO” of 910), the SCA risk determination operation ends. If the PVC metric meets the PVC threshold (“YES” of 910), processing circuitry 234 causes communication circuitry 506 to present an indication of increased SCA risk to the user (912). In some examples, communication circuitry 506 communicates with one or more of communication circuitry of external device 12 or computing devices 240A-240N to present the indication of increased SCA risk to the user.

[0109] FIG. 10 is a graph illustrating an example ECG comprising a potential PVC, which may be identified in accordance with one or more techniques of this disclosure. For each local maximum of the plurality of local maxima, processing circuitry 234 determines whether a curve associated with the local maximum corresponds to a PVC. ECG signal 1002 comprises a local maximum 1004.

[0110] Processing circuitry 234 can determine whether the curve associated with local maximum 1004 is a PVC by determining a rate of decay, e.g., a slope of decay, of the curve. Generally, PVCs present as lesser rates of decay, and R-waves present as greater rates of decay. Processing circuitry 234 may determine the rate of decay of the curve by determining a ratio between a peak amplitude 1006 and a second amplitude 1008. PVC peak amplitude 1006 corresponds to the amplitude of the peak at local maximum 1004. Second amplitude 1008 corresponds to the amplitude of the peak at some point after peak amplitude 1006, e.g., 10 samples after local maximum 1004. Based on the rate of decay, processing circuitry 234 determines whether the curve containing local maximum 1004 is a PVC. In some examples, processing circuitry 234 compares the rate of decay, i.e., the ratio of second amplitude 1008 to peak amplitude 1006, to a baseline rate of decay value. The baseline rate of decay value may correspond to a patient R-wave rate of decay. If the rate of decay value is different from the baseline rate of decay value according to a PVC decay threshold, e.g., 20% different, 25% different, 30% different, processing circuitry 234 determines the curve associated with local maximum 1004 is a PVC. Otherwise, processing circuitry 234 may determine the curve corresponds to an R-wave. In other examples, processing circuitry 234 may compare the rate of decay is greater than a threshold associated with typical patient rate of decay values, e.g., 0.9, to determine whether the curve is a PVC or a normally conducted R-wave.

[0111] Additionally, or alternatively, processing circuitry 234 can determine whether the curve associated with local maximum 1004 is a PVC by determining an area under the curve (AUC). The curve comprises a portion of the ECG transmission. The curve may be defined as the portion of the signal transmission between a certain number of samples before, e.g., 25 or 50, and a certain number of samples after, e.g., 25 or50, local maximum 904. In general, PVCs are associated with larger AUCs than normal R-waves. Processing circuitry 234 determines an AUC 1010 of the curve. If AUC 1010 is different from a baseline R-wave AUC according to a PVC AUC threshold, e.g., 20% different, 25% different, or 30% different, processing circuitry 234 determines the curve is a PVC. [0112] Additionally, or alternatively, processing circuitry 234 can determine whether the curve associated with local maximum 1004 is a PVC by determining a cumulative sum from local maximum 1004 to a baseline of the signal 1012. In some examples, processing circuitry 234 determines the baseline of the signal 1012 by applying a low pass filter or QRS segment suppression to the signal. Processing circuitry 234 compares the cumulative sum to a baseline cumulative sum to identify changes in the cumulative sum. If the changes in the cumulative sum meet a threshold or satisfy some criterion, processing circuitry 234 determines the curve is a PVC.

[0113] FIG. 11 is a graph illustrating an example ECG transmission comprising oversensed T-waves, which may be identified in accordance with one or more techniques of this disclosure. T-wave oversensing can occur, for example, due to improper lead placement or device sensitivity. The example ECG transmission comprises a signal over a predetermined period of time, e.g., 10 seconds. In other examples, the predetermined period of time may be longer, e.g., 30 seconds. The transmission may additionally or alternatively comprise a predetermined number of samples. ECG transmission signal 1106 comprises three peaks corresponding to nine oversensed T-waves 1102 and five peaks corresponding to 8 R-waves 1104. In some examples, processing circuitry of the system, e.g., processing circuitry 234 of computing system 230, may initially identify R-waves by identifying local maxima within the ECG transmission. During analysis, the system may differentiate between R-waves and oversensed T-waves.

[0114] FIG. 12 is a flow diagram illustrating an example operation for determining whether to adjust a configuration or placement of the IMD or to adjust PVC ECG analysis, e.g., operation described in FIG. 9, in response to T-wave oversensing. FIG. 12 may comprise a specific example of FIG. 6 or FIG. 8 wherein the one or more abnormal waveforms and/or other indicators of signal quality and/or analysis issues include one or more oversensed T-waves. FIG. 12 may alternatively comprise an operation responsive to the operation of FIG. 6 or FIG. 8, i.e., upon detection of abnormal waveforms, processing circuitry may initiate the operation described herein. In some examples, the operation of FIG. 12 may run concurrently with the operation of FIG. 6 and/or FIG. 8. FIG. 12 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 12 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A-240N.

[0115] According to the example of FIG. 12, computing system 230 receives an ECG transmission on a periodic schedule (1202). Processing circuitry 234 of computing system 230 determines whether one or more oversensed T-waves are present in the ECG transmission (1204). In some examples, to determine whether one or more oversensed T- waves are present in the ECG, processing circuitry 234 detects a plurality of local maxima within the ECG associated with a plurality of R-waves identified by IMD 10, and for each of the local maxima, processing circuitry determines whether a curve associated with the local maximum is an oversensed T-wave and not an R-wave. If there are no oversensed T- waves present in the transmission (“NO” of 1206), the operation ends. If there are oversensed T-waves present in the transmission (“YES” of 1206), processing circuitry 234 determines a T-wave oversensing metric (1208). The T-wave oversensing metric may comprise a quantification of the number of oversensed T-waves in the transmission. The T-wave oversensing metric may additionally be based on a confidence level associated with the oversensed T-wave identification. For example, if processing circuitry 234 is relatively confident, e.g., 95% confident, the T-wave oversensing metric may be higher than if processing circuitry 234 is less confident, e.g., 90%. Processing circuitry 234 compares the T-wave oversensing metric to a T-wave oversensing threshold (1210). If the T-wave oversensing metric does not meet the T-wave oversensing threshold (“NO” of 1210), the operation ends. If the T-wave oversensing metric meets the T-wave oversensing threshold (“YES” of 1210), processing circuitry 234 causes communication circuitry 506 to present an indication to the user, e.g., the clinician, that the placement or configuration of the IMD should be adjusted (1212).

[0116] In some examples, during implantation of the IMD, the clinician may determine whether the IMD has been placed properly by assessing T-wave oversensing using the example operation described herein. During implantation, the clinician may adjust the placement of the device to achieve a better signal. In examples in which the IMD has already been implanted, processing circuitry 234 may determine the configuration, e.g., the event detection sensitivity of the IMD, should be adjusted to account for any placement issues or patient physiology. In some examples, processing circuitry 234 may automatically adjust the event detection sensitivity. In other examples, processing circuitry 234 may prompt the clinician to adjust the sensitivity manually. The clinician may update the sensitivity using user interface 504. Processing circuitry 234 may suggest an updated sensitivity for clinician approval. In some examples, communication circuitry 506 communicates with one or more of communication circuitry of external device 12 or computing devices 240A-240N to present the indication to the clinician and to collect a clinician response.

[0117] Optionally, processing circuitry 234 may adjust the PVC analysis to account for known T-wave oversensing (1214). T-wave oversensing can cause inaccurate QT interval assessments. The QT interval, in addition to the presence of PVCs in the signal, can be indicative of a risk of SCA. In response to known T-wave oversensing, e.g., if T- wave oversensing is not resolved by adjusting IMD configuration or placement, processing circuitry 234 may adjust the PVC analysis to account for T-wave oversensing. For example, processing circuitry 234 may adjust any thresholds or metrics associated with PVC identification and quantification. Additionally, processing circuitry 234 may adjust the confidence associated with PVC identification, e.g., the confidence may be decreased if the ECG exhibits T-wave oversensing.

[0118] FIG. 13 is a graph illustrating an example ECG comprising a potential oversensed T-wave, which may be identified in accordance with one or more techniques of this disclosure. For each local maximum of the plurality of local maxima, processing circuitry 234 determines whether a curve associated with the local maximum corresponds to an oversensed T-wave. ECG signal 1302 comprises a local maximum 1304.

[0119] Processing circuitry 234 can determine whether the curve associated with local maximum 1304 is an oversensed T-wave by determining a rate of decay, e.g., a slope of decay, of the curve. Generally, R-waves correspond greater rates of decay than oversensed T-waves. Processing circuitry 234 may determine the rate of decay of the curve by determining a ratio between a potential oversensed T-wave peak amplitude 1206 and a potential oversensed T-wave second amplitude 1308. Potential oversensed T-wave peak amplitude 1206 corresponds to the amplitude of the peak at local maximum 1304. Potential oversensed T-wave second amplitude 1308 corresponds to the amplitude of the peak at some point after potential oversensed T-wave peak amplitude 1306, e.g., 10 samples after local maximum 1304. Based on the rate of decay, processing circuitry 234 determines whether the curve containing local maximum 1304 is an oversensed T-wave. In some examples, processing circuitry 234 compares the rate of decay, i.e., the ratio of potential oversensed T-wave second amplitude 1308 to potential oversensed T-wave peak amplitude 1306, to a baseline rate of decay value. The baseline rate of decay value may correspond to a patient R-wave rate of decay. If the rate of decay value is different from the baseline rate of decay value according to a T-wave decay threshold, e.g., 20% different, 25% different, 30% different, processing circuitry 234 determines the curve associated with local maximum 1304 is an oversensed T-wave. Otherwise, processing circuitry 234 may determine the curve corresponds to an R-wave. In other examples, processing circuitry 234 may determine the rate of decay is greater than a threshold associated with typical patient rate of decay values, e.g., 0.9, to determine whether the curve is an oversensed T-wave or an R-wave.

[0120] Additionally, or alternatively, processing circuitry 234 can determine whether the curve associated with local maximum 1304 is an oversensed T-wave by determining an area under the curve (AUC). The curve comprises a portion of the ECG transmission. The curve may be defined as the portion of the signal between a certain number of samples before, e.g., 25 or 50, and a certain number of samples after, e.g., 25 or 50, local maximum 804. In general, oversensed T-waves are associated with larger AUCs than typical R-waves. Processing circuitry 234 determines an AUC 1310 of the curve. If AUC 1310 is different from a baseline R-wave AUC according to a T-wave AUC threshold, e.g., 20% different, 25% different, 30% different, processing circuitry 234 determines the curve is an oversensed T-wave.

[0121] Additionally, or alternatively, processing circuitry 234 can determine whether the curve associated with local maximum 1304 is an oversensed T-wave by determining a cumulative sum from local maximum 1304 to a baseline of the signal 1312. Processing circuitry 234 compares the cumulative sum to a baseline cumulative sum to identify changes in the cumulative sum. If the changes in the cumulative sum meet a threshold or satisfy some criterion, processing circuitry 234 determines the curve is an oversensed T- wave.

[0122] FIG. 14 is a flow diagram illustrating an example operation for determining whether to adjust a configuration or placement of a medical device in response to baseline wandering. The operation described in FIG. 14 may run concurrently with one or more of the operations of FIG, 6, FIG. 8, or FIG. 12. FIG. 14 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 14 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A-240N.

[0123] According to the example of FIG. 14, computing system 230 receives an ECG transmission from IMD that was collected by IMD 10 on a periodic schedule (1402). In some examples, processing circuitry 234 may submit the ECG to one or more of QRS suppression or low pass filtering before analysis. Processing circuitry 234 of computing system 230 determines a baseline wandering metric based on the ECG signal (1404). If there is not baseline wandering in the transmission (“NO” of 1406), the operation ends. If there is baseline wandering in the transmission (“YES” of 1406), processing circuitry 234 causes communication circuitry 506 to present an indication to the user, e.g., the clinician, that the placement or configuration of the IMD should be adjusted to address the baseline wandering (1412).

[0124] In some examples, during implantation of the IMD, the clinician may determine whether the IMD has been placed properly by assessing baseline wandering using the example operation described herein. During implantation, the clinician may adjust the placement of the device to achieve a better signal. In examples in which the IMD has already been implanted, processing circuitry 234 may determine the configuration, e.g., the event detection sensitivity of the IMD should be adjusted to account for any placement issues or patient physiology. In some examples, processing circuitry 234 may automatically adjust the sensitivity. In other examples, processing circuitry 234 may prompt the clinician to adjust the sensitivity manually. The clinician may update the sensitivity using user interface 504. Processing circuitry 234 may suggest an updated sensitivity for clinician approval. In some examples, communication circuitry 506 communicates with one or more of communication circuitry of external device 12 or computing devices 240A-240N to present the indication to the clinician and to collect a clinician response.

[0125] FIG. 15 is a graph illustrating an example ECG comprising potential baseline wandering, which may be identified in accordance with one or more techniques of this disclosure. ECG signal 1504 exhibits baseline wandering. In some examples, to determine the baseline wandering metric, processing circuitry 234 may determine a number of zero voltage signal differential crossings. Zero voltage signal differential line 1502 comprises a baseline of the signal. Zero voltage signal differential crossing 1508 is one of several zero voltage signal differential crossings in the signal and corresponds to a time at which the ECG signal crosses zero voltage signal differential line 1502. Additionally, or alternatively, processing circuitry 234 may determine a time between zero voltage signal differential crossings, e.g., time 1506, to determine the baseline wandering metric. Additionally, or alternatively, processing circuitry 234 may determine a cumulative voltage differential between voltage signal differential crossings. Cumulative voltage differential areas 1510 are examples of elevated cumulative voltage differentials between zero crossings.

[0126] FIG. 16 is a flow diagram illustrating an example operation for determining whether to update a periodic schedule for ECG transmission. In some examples, greater or fewer regularly scheduled transmissions may be necessary for a patient, e.g., patient 4. For example, for patients with IMDs detecting a relatively large number of false positive cardiac events, processing circuitry may perform the operation described herein. Processing circuitry 234 determines a number of false positive cardiac event detections over some period of time, e.g., a week, a month, since implantation (1602). If the number of detections does not meet a detection threshold (“NO” of 1604), the operation ends, and processing circuitry 234 keeps the previous periodic schedule. If the number of detections meets the detection threshold (“YES” of 1604), processing circuitry updates the periodic schedule for ECG transmission, e.g., from once a day to twice a day (1606). In some examples, processing circuitry 234 additionally presents an indication to the user that the number of false positives is high.

[0127] FIG. 17 is a flow diagram illustrating an example operation for identifying a change in signal quality and/or analysis issues over time. FIG. 17 is described in the context of an example in which processing circuitry 234 of computing system 230 performs the example operation. In other examples, the operation of FIG. 17 may be performed in whole or in part by one or more devices, such as IMD 10, external device 12, or computing devices 240A-240N. [0128] To identify a change in signal quality and/or analysis issues over time, processing circuitry 234 may compare regularly scheduled ECG transmissions to a baseline transmission, e.g., a template transmission (1702). In some examples, the template transmission may be based on an ECG signal transmitted relatively soon after implantation, e.g., 30 days post-implantation. In some examples, the clinician and/or processing circuitry 234 may update the template transmission based on a change in disease state of patient 4. As an example, if patient 4 experiences a stroke, the clinician may choose to update the template transmission. Additionally, or alternatively, processing circuitry 234 may update the template transmission on a regular schedule, e.g., every six months. In some examples, to compare the first ECG transmission to the template transmission, processing circuitry 234 may determine one or more time features and/or perform a frequency analysis of the first ECG transmission and the template transmission. In some examples, to compare the first ECG transmission to the template transmission, processing circuitry 234 converts the ECG transmission to a transmission image and superimposes the transmission image over a template image. Processing circuitry 234 may identify differences between the two images by applying an X,Y coordinate system to the images and comparing each point. Additionally, or alternatively, to compare the first ECG transmission to the template transmission, processing circuitry 234 compares one or more temporal statistics, e.g., an R-wave mean, an R-wave median, an R-wave to R-wave intervals, a SNR, a P-wave to R-wave ratio, and/or a heart rate (HR), corresponding to the first ECG transmission to one or more corresponding temporal statistics of the template transmission. Additionally, or alternatively, to compare the first ECG transmission to the template transmission, processing circuitry 234 performs a frequency analysis by performing a wavelet decomposition of the first ECG transmission and comparing the wavelet decomposition of the first ECG transmission to a wavelet decomposition of the template transmission.

[0129] Processing circuitry 234 determines a first difference metric based on the comparison of the first ECG transmission to the template transmission (1704). The first difference metric may comprise a quantification of the differences between the one or more time features and/or the frequency analysis of the first ECG transmission and the template transmission. Processing circuitry 234 compares a second regularly scheduled ECG transmission to the template transmission in the same fashion (1706). Processing circuitry 234 determines a second difference metric based on the comparison of the second ECG transmission to the template transmission (1708). Processing circuitry 234 compares the first difference metric to the second difference metric (1710). Processing circuitry 234 determines whether signal quality has changed over time based on the comparison of the first difference metric and the second difference metric (1712). In some examples, processing circuitry 234 may additionally, or alternatively, compare the first ECG transmission and the second ECG transmission to determine whether signal quality has changed over time. The following is a non-limiting list of examples that are in accordance with one or more techniques of this disclosure.

[0130] Example 1. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determine an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, present an indication related to the determination that the ECG includes abnormal waveforms.

[0131] Example 2. The system of example 1, wherein one or more of the abnormal waveforms comprise one or more premature ventricular complexes (PVCs) [0132] Example 3. The system of example 2, processing circuitry is configured to determine the abnormal waveform metric based on a quantification of the abnormal waveforms.

[0133] Example 4. The system of any one or more of examples 2-3, wherein to determine one or more PVCs are present in the ECG, the processing circuitry is configured to: detect a plurality of local maxima within the ECG associated with a plurality of R- waves identified by the medical device; and for each of the plurality of local maxima, determine whether a curve associated with the local maximum is a PVC.

[0134] Example 5. The system of example 4, wherein to determine whether the curve associated with the local maximum is a PVC, the processing circuitry is configured to: for each of the local maxima, determine one or more PVC indicators; compare the one or more PVC indicators to one or more corresponding indicator thresholds; and responsive to the one or more PVC indicators meeting the one or more corresponding indicator thresholds, determine the local maximum is a PVC.

[0135] Example 6. The system of example 5, wherein one of the one or more PVC indicators comprises a rate of decay of a peak containing the local maximum in the ECG.

[0136] Example 7. The system of example 5, wherein one of the one or more PVC indicators comprises an area under the curve of a peak containing the local maximum in the ECG.

[0137] Example 8. The system of any one or more of examples 1-7, wherein the periodic schedule for ECG transmission comprises a nightly schedule. [0138] Example 9. The system of any one or more of examples 1-8, wherein the processing circuitry is further configured to: determine a number of false positive cardiac event detections; compare the number of false positive cardiac event detections to an event threshold; and in response to the number of false positive cardiac event detections meeting an event threshold, update the periodic schedule for ECG transmission.

[0139] Example 10. The system of any one or more of examples 1-9, wherein the medical device is an insertable cardiac monitor (ICM) comprising a plurality of housing electrodes, wherein the ICM is configured to sense the ECG via the plurality of housing electrodes.

[0140] Example 11. The system of any one or more of examples 1-10, wherein one or more of the abnormal waveforms is an oversensed T-wave.

[0141] Example 12. The system of example 11, wherein the processing circuitry is further configured to: based on the abnormal waveform metric meeting a T- wave oversensing threshold, present an indication to a user to adjust a configuration or a placement of the medical device.

[0142] Example 13. The system of any one or more of examples 11-12, wherein the processing circuitry is further configured to: based on the abnormal waveform metric meeting the T-wave oversensing threshold, update the analysis of the ECG.

[0143] Example 14. The system of any one or more of examples 1-13, wherein one or more of the abnormal waveforms comprise baseline wandering in the ECG. [0144] Example 15. The system of example 14, wherein to determine one or more of the abnormal waveforms comprise baseline wandering in the ECG, the processing circuitry is configured to: determine a count of zero voltage signal differential crossings. [0145] Example 16. The system of example 14, wherein to determine one or more of the abnormal waveforms comprise baseline wandering in the ECG, the processing circuitry is configured to: determine a time between zero voltage signal differential crossings.

[0146] Example 17. The system of example 14, wherein to determine one or more of the abnormal waveforms comprise baseline wandering in the ECG, the processing circuitry is configured to: determine a cumulative voltage differential between zero voltage signal differential crossings.

[0147] Example 18. The system of any one or more of examples 14-17, wherein the ECG has undergone one or more of QRS suppression or low pass filtering. [0148] Example 19. A method for operating a computing system, the method comprising: receiving, by processing circuitry of the computing system, a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determining, by the processing circuitry, one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determining, by the processing circuitry, an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, presenting, by the processing circuitry, an indication related to the determination that the ECG includes abnormal waveforms.

[0149] Example 20. The method of example 19, wherein one or more of the abnormal waveforms is a PVC.

[0150] Example 21. The method of any one or more of examples 19-20, wherein the abnormal waveform metric is based on a quantification of the abnormal waveforms.

[0151] Example 22. The method of any one or more of examples 20-21, wherein determining one or more PVCs are present in the ECG comprises: detecting, by the processing circuitry, a plurality of local maxima within the ECG associated with a plurality of R- waves identified by the medical device; and for each of the plurality of local maxima, determining, by the processing circuitry, whether a curve associated with the local maximum is a PVC.

[0152] Example 23. The method of example 22, wherein determining whether the curve associated with the local maximum is a PVC comprises: for each of the local maxima, determining, by the processing circuitry, one or more PVC indicators; comparing, by the processing circuitry, the one or more PVC indicators to one or more corresponding indicator thresholds; and responsive to the one or more PVC indicators meeting the one or more corresponding indicator thresholds, determining, by the processing circuitry, the local maximum is a PVC.

[0153] Example 24. The method of example 23, wherein one of the one or more PVC indicators comprises a rate of decay of a peak containing the local maximum in the ECG.

[0154] Example 25. The method of example 23, wherein one of the one or more PVC indicators comprises an area under the curve of a peak containing the local maximum in the ECG.

[0155] Example 26. The method of any one or more of examples 19-25, wherein the periodic schedule for ECG transmission comprises a nightly schedule.

[0156] Example 27. The method of any one or more of examples 19-26, further comprising: determining, by the processing circuitry, a number of false positive cardiac event detections; comparing, by the processing circuitry, the number of false positive cardiac event detections to an event threshold; and in response to the number of false positive cardiac event detections meeting an event threshold, updating, by the processing circuitry, the periodic schedule for ECG transmission.

[0157] Example 28. The method of any one or more of examples 19-27, wherein the medical device is an insertable cardiac monitor (ICM) comprising a plurality of housing electrodes, wherein the ICM is configured to sense the ECG via the plurality of housing electrodes.

[0158] Example 29. The method of any one or more of examples 19-28, wherein one or more of the abnormal waveforms is an oversensed T-wave.

[0159] Example 30. The method of example 29, further comprising: based on the abnormal waveform metric meeting a T-wave oversensing threshold, presenting an indication to a user to adjust a configuration or a placement of the medical device. [0160] Example 31. The method of example 29, further comprising: based on the abnormal waveform metric meeting a T-wave oversensing threshold, updating, by the processing circuitry, the analysis of the ECG.

[0161] Example 32. The method of any one or more of examples 19-31, wherein one or more of the abnormal waveforms comprise baseline wandering in the ECG.

[0162] Example 33. The system of example 32, wherein determining one or more of the abnormal waveforms comprise baseline wandering in the ECG comprises: determining, by the processing circuitry, a count of zero voltage signal differential crossings.

[0163] Example 34. The system of example 32, wherein determining one or more of the abnormal waveforms comprise baseline wandering in the ECG comprises: determining, by the processing circuitry, a time between zero voltage signal differential crossings.

[0164] Example 35. The system of example 32, wherein determining one or more of the abnormal waveforms comprise baseline wandering in the ECG comprises: determining, by the processing circuitry, a cumulative voltage differential between zero voltage signal differential crossings.

[0165] Example 36. The system of any one or more of examples 32-35, wherein the ECG has undergone one or more of QRS suppression or low pass filtering. [0166] Example 37. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause processing circuitry of a computing system to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more abnormal waveforms are present in the ECG; based on the determination that one or more abnormal waveforms are present, determine an abnormal waveform metric; and based on the abnormal waveform metric meeting a threshold, present an indication related to the determination that the ECG includes abnormal waveforms.

[0167] Example 38. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more premature ventricular complexes (PVCs) are present in the ECG; based on the determination that one or more PVCs are present, determine a PVC metric; and based on the PVC metric meeting a threshold, present an indication to a user that the patient is at an elevated risk of experiencing sudden cardiac arrest (SCA).

[0168] Example 39. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more oversensed T-waves are present in the ECG; based on the determination that one or more oversensed T-waves are present, determine a T-wave oversensing metric; and based on the T-wave oversensing metric meeting a threshold, present an indication to a user to adjust a configuration or a placement of the medical device.

[0169] Example 40. The system of example 39, wherein to determine one or more oversensed T-waves are present in the ECG, the processing circuitry is configured to: detect a plurality of local maxima within the ECG associated with a plurality of R- waves identified by the medical device; and for each of the plurality of local maxima, determine whether the local maximum is a T-wave.

[0170] Example 41. The system of any one or more of examples 39-40, wherein the processing circuitry is configured to determine the T-wave oversensing metric based on a quantification of T-waves.

[0171] Example 42. The system of any one or more of examples 40-41, wherein to determine whether the local maximum is a T-wave, the processing circuitry is configured to: for each of the plurality of local maxima, determine one or more T-wave oversensing indicators; compare the one or more T-wave oversensing indicators to one or more corresponding indicator thresholds; and responsive to the one or more T-wave oversensing indicators meeting the one or more corresponding indicator thresholds, determine the local maximum is a T-wave.

[0172] Example 43. The system of example 42, wherein one of the one or more T-wave oversensing indicators comprises a rate of decay of a peak containing the local maximum in the ECG. [0173] Example 44. The system of example 42, wherein one of the one or more T-wave oversensing indicators comprises an area under the curve of a peak containing the local maximum in the ECG.

[0174] Example 45. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine a baseline wandering metric based on the ECG; and based on the baseline wandering metric meeting a threshold, present an indication to a user to adjust a configuration or a placement of the medical device.

[0175] Example 46. The system of example 45, wherein the processing circuitry is configured to determine the baseline wandering metric based on a count of zero voltage signal differential crossings.

[0176] Example 47. The system of example 45, wherein the processing circuitry is configured to determine the baseline wandering metric based on a time between zero voltage signal differential crossings.

[0177] Example 48. The system of example 45, wherein the processing circuitry is configured to determine the baseline wandering metric based on a cumulative voltage differential between zero voltage signal differential crossings.

[0178] Example 49. The system of any one or more of examples 45-48, wherein the ECG has undergone one or more of QRS suppression or low pass filtering. [0179] Example 50. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

[0180] Example 51. The system of example 50, wherein the processing circuitry is configured to determine the signal quality issue metric based on a quantification of the one or more signal quality issues. [0181] Example 52. The system of example 51, wherein to determine the signal quality issue metric based on the quantification of the one or more signal quality issues, the processing circuitry is configured to: determine a metric for each of the one or more signal quality issues, wherein the one or more metrics comprise one or more of: a T- wave oversensing metric, a baseline wandering metric, a baseline noise metric, a signal to noise ratio metric, a P-wave presence metric, an R-wave presence metric, an R-wave amplitude metric, or a potential contact loss metric; for each of the one or more metrics, compare the metric to a corresponding metric threshold; and based on the metric meeting the metric threshold, include the metric in the quantification of the signal quality issue metric.

[0182] Example 53. The system of any one or more of examples 50-52, wherein the processing circuitry is further configured to: present an indication to the user to adjust a configuration of the medical device.

[0183] Example 54. The system of any one or more of examples 50-53, wherein the processing circuitry is configured to identify a change in signal quality over time based on a comparison of one or more regularly scheduled ECG transmissions to a baseline transmission.

[0184] Example 55. The system of example 54, wherein to identify the change in signal quality over time over time based on the comparison of the one or more regularly scheduled ECG transmissions to the baseline transmission, the processing circuitry is configured to: compare a first regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly scheduled ECG transmission to the baseline transmission, determine a first difference metric; compare a second regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly schedule ECG transmission to the baseline transmission, determine a second difference metric; and compare the first difference metric to the second difference metric [0185] Example 56. The system of example 55, wherein to compare the first regularly scheduled ECG transmission to the template transmission, the processing circuitry is configured to one or more of: compare one or more time features of the first regularly scheduled ECG transmission to one or more time features of the template transmission; or compare a frequency analysis of the first regularly scheduled ECG transmission to a frequency analysis of the template transmission.

[0186] Example 57. The system of any one or more of examples 50-56, wherein the processing circuitry is further configured to: determine a number of false positive cardiac event detections; compare the number of false positive cardiac event detections to an event threshold; and in response to the number of false positive cardiac event detections meeting an event threshold, update the periodic schedule for ECG transmission.

[0187] Example 58. The system of any one or more of examples 50-57, wherein the periodic schedule for ECG transmission comprises a nightly schedule.

[0188] Example 59. The system of any one or more of examples 50-58, wherein the medical device is an insertable cardiac monitor (ICM) comprising a plurality of housing electrodes, wherein the ICM is configured to sense the ECG via the plurality of housing electrodes.

[0189] Example 60. A method comprising: receiving, by processing circuitry of a computing system, a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determining, by the processing circuitry, one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determining, by the processing circuitry, a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, presenting, by the processing circuitry, an indication to a user related to the determination that the ECG includes signal quality issues.

[0190] Example 61. The method of example 60, wherein the signal quality issue metric is based on a quantification of the one or more signal quality issues.

[0191] Example 62. The method of example 61, wherein determining the signal quality issue metric based on the quantification of the one or more signal quality issues comprises: determining, by the processing circuitry, for each of the signal quality issues, a metric, wherein the one or more metrics comprise one or more of: a T-wave oversensing metric, a baseline wandering metric, a baseline noise metric, a signal to noise ratio metric, a P-wave presence metric, a R-wave presence metric, an R-wave amplitude metric, or a potential contact loss metric; for each of the one or more metrics, comparing, by the processing circuitry, the metric to a corresponding metric threshold; and based on the metric meeting the metric threshold, including, by the processing circuitry, the metric in the quantification of the signal quality issue metric.

[0192] Example 63. The method of any one or more of examples 60-62, further comprising: presenting, by the processing circuitry, an indication to the user to adjust a configuration of the medical device.

[0193] Example 64. The method of any one or more of examples 60-63, wherein the processing circuitry is configured to identify a change in signal quality over time based on a comparison of one or more regularly scheduled ECG transmissions to a baseline transmission.

[0194] Example 65. The method of example 64, wherein identifying the change in signal quality over time over time based on the comparison of the one or more regularly scheduled ECG transmissions to the baseline transmission comprises: comparing, by the processing circuitry, a first regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly scheduled ECG transmission to the baseline transmission, determining, by the processing circuitry, a first difference metric; comparing, by the processing circuitry, a second regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly schedule ECG transmission to the baseline transmission, determining, by the processing circuitry, a second difference metric; and comparing, by the processing circuitry, the first difference metric to the second difference metric.

[0195] Example 66. The method of example 65, wherein comparing the first regularly scheduled ECG transmission to the template transmission, the processing circuitry comprises one or more of: comparing, by the processing circuitry, one or more time features of the first regularly scheduled ECG transmission to one or more time features of the template transmission; or comparing, by the processing circuitry, a frequency analysis of the first regularly scheduled ECG transmission to a frequency analysis of the template transmission.

[0196] Example 67. The method of any one or more of examples 60-66, further comprising: determining, by the processing circuitry, a number of false positive cardiac event detections; comparing, by the processing circuitry, the number of false positive cardiac event detections to an event threshold; and in response to the number of false positive cardiac event detections meeting an event threshold, updating, by the processing circuitry, the periodic schedule for ECG transmission.

[0197] Example 68. The method of any one or more of examples 60-67, wherein the periodic schedule for ECG transmission comprises a nightly schedule.

[0198] Example 69. A non-transitory computer-readable medium storing instructions that when executed by processing circuitry cause the processing circuitry to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

[0199] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

WHAT IS CLAIMED:

1. A computing system comprising processing circuitry configured to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.

2. The system of claim 1, wherein the processing circuitry is configured to determine the signal quality issue metric based on a quantification of the one or more signal quality issues.

3. The system of claim 2, wherein to determine the signal quality issue metric based on the quantification of the one or more signal quality issues, the processing circuitry is configured to: determine a metric for each of the one or more signal quality issues, wherein the one or more metrics comprise one or more of: a T-wave oversensing metric, a baseline wandering metric, a baseline noise metric, a signal to noise ratio metric, a P-wave presence metric, an R-wave presence metric, an R-wave amplitude metric, or a potential contact loss metric; for each of the one or more metrics, compare the metric to a corresponding metric threshold; and based on the metric meeting the metric threshold, include the metric in the quantification of the signal quality issue metric.

4. The system of any one or more of claims 1-3, wherein the processing circuitry is further configured to: present an indication to the user to adjust a configuration of the medical device.

5. The system of any one or more of claims 1-4, wherein the processing circuitry is configured to identify a change in signal quality over time based on a comparison of one or more regularly scheduled ECG transmissions to a baseline transmission.

6. The system of claim 5, wherein to identify the change in signal quality over time based on the comparison of the one or more regularly scheduled ECG transmissions to the baseline transmission, the processing circuitry is configured to: compare a first regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly scheduled ECG transmission to the baseline transmission, determine a first difference metric; compare a second regularly scheduled ECG transmission of the one or more ECG transmissions to the baseline transmission; based on the comparison of the first regularly schedule ECG transmission to the baseline transmission, determine a second difference metric; and compare the first difference metric to the second difference metric.

7. The system of claim 6, wherein to compare the first regularly scheduled ECG transmission to the baseline transmission, the processing circuitry is configured to one or more of: compare one or more time features of the first regularly scheduled ECG transmission to one or more time features of the baseline transmission; or compare a frequency analysis of the first regularly scheduled ECG transmission to a frequency analysis of the baseline transmission.

8. The system of any one or more of claims 5-7, wherein the baseline transmission comprises a template transmission.

9. The system of any one or more of claims 1-8, wherein the processing circuitry is further configured to: determine a number of false positive cardiac event detections; compare the number of false positive cardiac event detections to an event threshold; and in response to the number of false positive cardiac event detections meeting an event threshold, update the periodic schedule for ECG transmission.

10. The system of any one or more of claims 1-9, wherein the periodic schedule for ECG transmission comprises a nightly schedule.

11. The system of any one or more of claims 1-10, wherein the medical device is an insertable cardiac monitor (ICM) comprising a plurality of housing electrodes.

12. The system of claim 11, wherein the ICM is configured to sense the ECG via the plurality of housing electrodes.

13. A non-transitory computer-readable medium storing instructions that when executed by processing circuitry cause the processing circuitry to: receive a regularly scheduled electrocardiogram (ECG) transmission from a medical device, wherein the regularly scheduled ECG transmission includes an ECG that was sensed by the medical device in response to a periodic schedule; determine one or more signal quality issues are present in the ECG; based on the determination that one or more signal quality issues are present, determine a signal quality issue metric; and based on the signal quality issue metric meeting a threshold, present an indication to a user related to the determination that the ECG includes signal quality issues.