System for determining that ventricular tachyarrhythmia is predicted

Abstract

A medical device system includes an implantable medical device configured to sense cardiac electrogram data and processing circuitry. The processing circuitry is configured to identify a premature ventricular contraction event within the cardiac electrogram data. The processing circuitry is configured to determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data. The processing circuitry is configured to determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics. The processing circuitry is configured to determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

Description

SYSTEM FOR DETERMINING THAT VENTRICULAR TACHYARRHYTHMIA IS PREDICTED

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 637,267, filed April 22, 2024, the entire content of which is incorporated herein by reference.FIELD

[0002] The disclosure relates generally to medical device systems.BACKGROUND

[0003] Medical devices may be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense cardiac electrogram (EGM) signals indicative of the electrical activity of the heart via electrodes. Some medical devices may be configured to deliver a therapy in conjunction with or separate from the monitoring of physiological signals. For example, implantable cardioverter-defibrillators (ICDs) can sense abnormal heart rhythms and deliver life-saving therapies like electrical shocks to restore normal rhythm.

[0004] Cardiovascular disorders include life-threatening conditions such as arrhythmias, ischemic heart disease, and heart failure. Early detection through continuous monitoring allows for prompt intervention, such as defibrillation for ventricular tachycardia and fibrillation, significantly reducing the risk of sudden cardiac death. Additionally, cardiac monitoring plays a crucial role in assessing cardiovascular risk, ensuring timely interventions and preventive measures to mitigate complications and improve overall cardiac health.SUMMARY

[0005] In some examples, a medical device system includes an implantable medical device configured to sense cardiac electrogram data; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulencemetrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0006] In some examples, a method includes sensing, by an implantable medical device, cardiac electrogram data; identifying, by processing circuitry, a premature ventricular contraction event within the cardiac electrogram data; determining, by the processing circuitry and for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determining, by the processing circuitry and for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determining, by the processing circuitry, whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0007] In some examples, an implantable medical device includes sensing circuitry configured to sense a cardiac electrogram data of the patient; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0008] The 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 systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below.Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. l is a conceptual diagram illustrating an example medical device system in accordance with techniques of this disclosure.

[0010] FIG. 2 is a functional block diagram illustrating an example configuration of an implantable medical device (IMD) in accordance with techniques of this disclosure.

[0011] FIG. 3 is a chart illustrating cardiac electrogram data and a dT angle calculated in accordance with techniques of this disclosure.

[0012] FIG. 4 is a conceptual diagram illustrating a dT angle.

[0013] FIGS. 5A-5B are box and whisker plots of the distribution of a set of dT angles associated with a PVC event.

[0014] FIG. 6 is a functional block diagram illustrating an example configuration of an external device in accordance with techniques of this disclosure.

[0015] FIG. 7 is a block diagram illustrating an example system that includes an access point, a network, external computing devices, such as a server, and one or more other computing devices, which may be coupled to an IMD and external device.

[0016] FIG. 8 is a flow diagram illustrating an example operation for detecting a cardiac disorder in accordance with techniques of this disclosure.

[0017] Like reference characters denote like elements throughout the description and figures.DETAILED DESCRIPTION

[0018] Sudden cardiac death (SCD) is a catastrophic event that occurs when the heart suddenly stops beating effectively. SCD (e.g., sudden cardiac arrest) can happen within minutes after the onset of arrhythmias, highlighting the importance of early detection and intervention. SCD often occurs without warning, especially in individuals with underlying heart disease or other risk factors. Risk factors include ventricular tachycardia (VT) and ventricular fibrillation (VF), referred to collectively as ventricular tachyarrhythmia, which are serious types of arrhythmias that can degenerate into cardiac arrest and death.

[0019] In accordance with techniques of this disclosure, a system may determine a metric of turbulence (change associated with a premature ventricular contraction (PVC) or other perturbation) of periodic repolarization dynamics (PRD) to assess the risk for SCD as well as the occurrence of VT / VF. As used herein, PRD refers to assessing changes in the repolarization phase of the cardiac cycle, particularly in response to sympathetic nervous system activity. PRD primarily involves analyzing variations in the morphology and duration of the T-wave in cardiac electrogram (EGM) data. Metrics of PRD may include T-wave morphological metrics, QT or other activation / repolarization intervals, or dT angle. Turbulence metrics may assess the response of the heart rate to PVCs, which are extra, abnormal heartbeats that disrupt the regular rhythm. The techniques of this disclosure include determining a metric of turbulence of a PRD metric, similar to how heart rate turbulence (HRT) is determined using HR metrics.

[0020] For example, the system may calculate a change in angle between successive repolarization vectors (i.e., a dT angle) as a PRD metric. The dT angle may represent an estimate of the instantaneous repolarization instability. Based on changes (turbulence) in the dT angle when a PVC occurs, the system may evaluate a risk of SCD and / or VT / VF.

[0021] The techniques of this disclosure may yield various advantages, particularly with respect to the continuous monitoring and follow-up of patients at risk of SCD. For example, the techniques have the potential to reduce administering electrical cardioversion or defibrillation to restore normal heart rhythm in patients since the system may predict the occurrence of VT / VF events, and enable intervention to avoid such events. Continuous and accurate monitoring is especially important for conditions such as SCD given the severity and suddenness of the condition. The techniques may also guide the medication of patients by providing indications of sympathovagal imbalance. Furthermore, the techniques may help identify patients who are at risk of SCD and VT / VF events and thus can benefit from a defibrillation device, such as a wearable automated external defibrillator (WAED) or implantable cardioverter defibrillator (ICD), e.g., benefit from an upgrade to or addition of an ICD device (e.g., from an insertable cardiac monitor (ICM), implantable pulse generator, cardiac resynchronization therapy (CRT) device, etc.).

[0022] FIG. l is a conceptual diagram illustrating an example of a medical device system 2 (“system 2”) in accordance with techniques of this disclosure. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in thepectoral 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. IMD 10 may include a plurality of electrodes configured to sense electrical signals (e.g., a cardiac electrogram (EGM)). In some examples, IMD 10 may be a cardiac monitor. Although primarily described in the context of examples in which IMD 10 is an ICM or other cardiac monitor that does not necessarily deliver therapy, in other examples IMD 10 may be a leaded or leadless pacemaker, ICD, or other therapy device. In any case, IMD 10 may be configured to sense a cardiac EGM and analyze cardiac EGM data.

[0023] External device 12 is a computing device configured for wireless communication with IMD 10. External device 12 may be, as examples, a mobile telephone or other computing device of patient 4 or another user, or a computing device detected to communication with IMD 10. External device 12 may be configured to communicate with a computing system 23 via a network 25. In some examples, external device 12 may provide a user interface and allow a user to interact with IMD 10. Computing system 23 may comprise computing devices configured to allow a user to interact with IMD 10, or data collected from IMD 10, via network 25.

[0024] In some examples, computing system 23 includes one or more handheld computing devices, computer workstations, servers or other networked computing devices. In some examples, computing system 23 may include one or more devices, including processing circuitry and storage devices, that implement a monitoring system 450.Computing system 23, network 25, and monitoring system 450 may be implemented by the Medtronic Carelink™ Network or other patient monitoring system, in some examples.

[0025] SCD is the single most common cause of death in the industrialized world but is difficult to predict. A substantial proportion of SCD cases occur in patients after myocardial infarction (MI). Prophylactic implantation of an IMD, such as an ICD, can effectively reduce mortality in high-risk patients after MI. Therefore, a significant advantage of the configuration of system 2 is its ability to identify of high-risk individuals, who may then be prophylactically implanted with an ICD or provided another device or treatment.

[0026] In accordance with techniques of this disclosure, system 2 may include processing circuitry configured to calculate a PRD metric and PRD turbulence (a metric analogous to HRT) to assess risk for SCD. System 2 may calculate oscillations of therepolarization vector (T-wave) dynamics and in turn detect instability associated with sympathetic activity. System 2 may calculate a PRD metric that indicates dispersion in repolarization (associated with sympathetic tone). System 2 may also calculate changes in the PRD metric (e.g., the dT angle) induced by a PVC. PRD turbulence may measure the physiological response to system disturbances and may indicate the sympathovagal imbalance in a more systemic way.

[0027] For example, system 2 may calculate a dT angle as a PRD metric. The dT angle metric may indicate or otherwise constitute the change in the three-dimensional angle in the repolarization vector between two consecutive cardiac cycles. System 2 may then calculate changes in the dT angle to determine a PRD turbulence to output a potentially more accurate prediction of SCD and VT / VF events (e.g., compared to HRT, which uses an R-R interval (RRI) as a base for calculations). In this way, system 2 may more effectively evaluate the risk of a SCD and / or VT / VF event, ensuring timely interventions and preventive measures to mitigate complications and improve overall cardiac health.

[0028] FIG. 2 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 51, which may be located on one or more leads and / or a housing of IMD 10, processing circuitry 50, sensing circuitry 52, communication circuitry 54, and storage device 56, as well as optional therapy delivery circuitry 58 and sensors 62.

[0029] Processing circuitry 50 may include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 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, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof.

[0030] Sensing circuitry 52 may be coupled to electrodes 51. Sensing circuitry 52 may sense signals from electrodes 51, e.g., to produce a cardiac EGM, in order to facilitate monitoring the electrical activity of the heart. Sensing circuitry 52 also may monitorsignals from sensors 62, which may include one or more accelerometers, pressure sensors, and / or optical sensors, as examples. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 51 and / or sensors 62. Sensing circuitry 52 may include switching circuitry for selecting electrodes 51 and their polarity for sensing the cardiac EGM signals described herein.

[0031] Sensing circuitry 52 and / or processing circuitry 50 may be configured to detect cardiac electrical activity including depolarizations (e.g., R-waves or QRS complexes) and repolarizations (e.g., T-waves). For instance, processing circuitry 50 may be configured to receive the cardiac electrogram data sensed by sensing circuitry 52 of IMD 10. In some examples, (e.g., when sensing circuitry 52 is coupled to electrodes 51 arranged to collect such data) processing circuitry 50 may be configured to determine the vectorcardiography (VCG) of cardiac electrical activity (e.g., cardiac EGM data) by representing cardiac activity in three-dimensional space. Sensing circuitry 52 may be configured to measure the amplitude and direction of electrical signals sensed by electrodes 51, and processing circuitry 50 may construct a vector loop representing the cardiac electrical activity. The vector loop may provide information about the depolarization and repolarization processes of the heart in three dimensions.

[0032] To measure the T-wave, processing circuitry 50 may analyze the portion of the vector loop corresponding to the T-wave. The T-wave represents the repolarization phase of the ventricles, and its duration, morphology, and amplitude can provide insights into cardiac health. In general, changes in the magnitude and direction of the T-wave vector may indicate abnormalities in ventricular repolarization. For example, changes in the amplitude or direction of the T-wave vector may suggest conditions such as myocardial ischemia, electrolyte imbalances, ventricular hypertrophy, etc. Processing circuitry 50 (or processing circuitry of another device configured to retrieve data from IMD 10) may process the T-wave data to determine SCD and / or VT / VF is predicted, e.g., to determine a risk of SCD and / or VT / VF, in accordance with techniques with of this disclosure.

[0033] Communication circuitry 54 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 50, communication circuitry 54 may receivedownlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna. In addition, processing circuitry 50 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 54 may be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0034] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. Storage device 56 may store, as examples, programmed values for one or more operational parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include digitized cardiac EGMs.

[0035] Processing circuitry 50 may be configured to control therapy delivery circuitry 58 to generate and deliver electrical therapy to heart 8 of the patient via electrodes 51. Electrical therapy may include, for example, pacing pulses, or any other suitable electrical stimulation. Processing circuitry 50 may control therapy delivery circuitry 58 to deliver electrical stimulation therapy via electrodes 51 according to one or more therapy parameter values, which may be stored in storage device 56. The therapy parameter values may, in the case of pacing pulses, include magnitude values, such as pulse amplitude (e.g., a stimulation voltage amplitude) and width. Therapy delivery circuitry 58 may include capacitors, current sources, and / or regulators, in some examples. Therapy delivery circuitry 58 may include switching circuitry for selecting electrodes 51 and their polarity for delivering therapy signals to a patient’s heart.

[0036] FIG. 3 is a chart illustrating example EGM 64 sensed by electrodes 51 as well as a dT angle 66 (“dT angle 66”) calculated by processing circuitry 50 based on the time-corresponding EGM 64. EGM 64 may be displayed as a waveform over time, with the amplitude reflecting the strength of electrical impulses and the time scale marking the duration of the cardiac cycle. Analysis of EGM 64 may denote significant cardiac events such as depolarization (P wave), ventricular depolarization (QRS complex), and repolarization (T wave). In general, a normal beat on EGM 64 appears as a regular and orderly waveform representing the sequential depolarization and repolarization of the heart’s chambers.

[0037] Processing circuitry 50 may be configured to identify one or more PVC events within EGM 64 collected by electrodes 51. For example, EGM 64 may include one or more PVCs, such as PVC 68A and PVC 68B (collectively, “PVCs 68”). PVCs manifest as irregular waveforms disrupting the typical pattern of cardiac electrical activity. PVCs are characterized by premature depolarization of the ventricles, leading to wide QRS complexes on the EGM due to abnormal ventricular activation. In some examples, a compensatory pause may follow a PVC and before the next regular heartbeat, allowing the heart to reset its rhythm. In other words, PVC may consist of a brief acceleration in the heart rate followed by a gradual deceleration. In general, a PVC is an example of a cardiovascular system disturbance, the response to which system 2 may evaluate.

[0038] Processing circuitry 50 may determine, for at least one or more cardiac cycles associated with a PVC event, one or more PRD metrics based on the cardiac EGM data. For example, processing circuitry 50 may determine dT angle 66 (e.g., based on VCG). dT angle 66, which processing circuitry 50 may calculate continuously, may also appear as a regular and orderly waveform with a relatively consistent amplitude. However, during PVC, which denotes a perturbation of the parasympathetic system, or some other sympathetic activity, dT angle 66 may deviate from normal patterns (e.g., the amplitude of dT angle 66 may significantly increase). These deviation may indicate underlying cardiac pathology.

[0039] Processing circuitry 50 may determine, for at least one or more cardiac cycles associated with a PVC event, one or more turbulence metrics based on the one or more PRD metrics. For example, processing circuitry 50 may determine a turbulence metric or other perturbation-responsive change in PRD by analyzing the deviation of dT angle 66, particularly around the occurrence of PVC, to assess the risk of SCD and / or VT / VF. For example, when PVC 68A happens, processing circuitry 50 may identify one or morecardiac cycles, and in turn dT angles, associated with PVC 68A. Processing circuitry 50 may identify the cardiac cycles within a predetermined number (e.g., 3, 4, 5, etc.) of cardiac cycles of PVC 68 A as the cardiac cycles associated with PVC 68 A. Processing circuitry 50 may then determine PRD based on the one or more dT angles associated with PVC 68A, such as by comparing the one or more dT angles associated with PVC 68A to normal (e.g., average) dT angles.

[0040] Processing circuitry 50 may determine the risk of SCD and / or VT / VF based at least in part on the one or more calculated PRD metrics. For example, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted if one or more of the dT angles associated with the occurrence of PVC 68 A is greater than a dT angle threshold. Additionally or alternatively, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted if one or more of the dT angles associated with the occurrence of PVC 68 A deviates by a predetermined amount (in percentage or absolute terms) from normal dT angles.

[0041] Processing circuitry 50 may also use the dT angles or other repolarization metrics associated with the occurrence of PVC 68A to calculate a turbulence metric (e.g., a PRD turbulence metric). That is, processing circuitry 50 may calculate a turbulence metric using a repolarization dispersion metric (e.g., based on the PRD metrics) instead of a beat-to-beat interval, thereby potentially outputting a more accurate prediction of SCD and VT / VF events. For example, as discussed earlier, processing circuitry 50 may calculate changes in dT angle as a base for the PRD calculation. Processing circuitry 50 may then use the dT angle metric instead of RRI to calculate one or more metrics associated with turbulence, which may include turbulence onset (TO) and turbulence slope (TS).

[0042] TO may be based on an average of one or more dT angles associated with one or more cardiac cycles preceding a premature ventricular contraction event, and an average of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event. For example, processing circuitry 50 may calculate TO as:where dTbaseiine is an average of dT values before occurrence of PVC, and dTiater is an average of dT angles immediately following the PVC.TS may be based on a linear regression of one or more dT angles associated with one or more cardiac cycles immediately following a premature ventricular contraction event. For example, TS may be calculated by identifying the steepest slope of linear regression in a series of five consecutive dT angles within the first 15 to 20 dT angles following a PVC. For example, TS may be the steepest slope of the possible linear regressions created by using any five consecutive dT angles (e.g., 1-5, 2-6, 3-7, 4-8, etc.) within the first 20 cardiac cycles immediately following the PVC. In other examples, processing circuitry 50 may calculate TO and TS by analyzing changes in dT angles instead of analyzing the dT angles themselves. For example, dT may be substituted with AdT in the above calculations or techniques, where AdT = dTi+i - dTi or AdT = |dTi+i - dTi| .

[0043] Abnormalities in the turbulence metrics, such as TO and TS, can indicate potential problems with cardiac function and may help in detecting certain cardiovascular conditions. As such, processing circuitry 50 may evaluate the risk of a SCD and / or VT / VF event based on these turbulence metrics in conjunction with the PRD metrics. For example, if the TO values are too large, reflecting an inability of the autonomic nervous system to quickly regulate heart rate after PVC, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted. Similarly, if the TS values deviate significantly from expected values or falls outside predetermined ranges, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted.

[0044] In any case, system 2 may routinely, if not continuously, measure PRD and turbulence metrics to monitor a patient’s risk for heart rhythm disorders. Furthermore, system 2 may use the techniques of this disclosure to monitor a patient’s response to pharmacological or interventional treatments for heart rhythm disorders. System 2 may determine whether to provide an indication of SCD and / or VT / VF based on the PRD and turbulence metrics. System 2 may also determine the effectiveness of treatment strategies in stabilizing cardiac rhythm and reducing the risk of adverse events based on changes in the PRD and turbulence metric values over time, potentially provide insights that may aid in risk stratification, prognostication, and treatment optimization in patients with cardiovascular diseases.

[0045] FIG. 4 is a conceptual diagram illustrating a dT angle. The dT parameter may indicate the change in the three-dimensional angle in the repolarization vector between two consecutive cardiac cycles. Processing circuitry 50 (or other processing circuitry of system 2) may determine the repolarization vector to visualize the electrical activity of the heart in three dimensions using vectors using any suitable technique, such as VCG. In some examples, system 2 may use one or more leads to measure the electrical signals from different angles around the body. Processing circuitry 50 may combine the electrical signals to calculate a resultant vector representing the overall direction and magnitude of the electrical activity during the T-wave.

[0046] In VCG, the T-wave represents the repolarization phase of the cardiac cycle. The T-wave is generated as the ventricles repolarize and regain a resting electrical state after depolarization. The direction and magnitude of the T-wave vector reflect the pattern of repolarization in the heart. Changes in the direction or magnitude of the T-wave vector may indicate abnormalities in repolarization and can be suggestive of risk for certain arrhythmias (e.g., VT / VF). For example, processing circuitry 50 may analyze changes in a dT angle around when PVC occurs to comprehensively assess cardiac health and function (e.g., determine a risk of SCD and / or VT / VF).

[0047] FIGS. 5A-5B are box and whisker plots of the distribution of a set of dT angles associated with a PVC event (e.g., dT angles from N beats before the PVC event to N beats after the PVC event, where N can be any number, such as 2, 3, 4, 5, etc.). FIG. 5A is a box plot of dT angles for a patient who does not possess an increased risk of SCD and / or VT / VF. FIG. 5B is a box plot of the dT angles for a patient who does possess an increased risk of SCD and / or VT / VF.

[0048] As shown by FIGS. 5A-5B, key statistical measures such as the median, quartiles, and potential outliers, differ between patients with no increased risk of VT / VF and patients with an increased risk of VT / VF. For example, the minimum value, the interquartile range (i.e., the range between the first and third quartiles), the median, and the maximum value in FIG. 5B are greater than the same for FIG. 5 A. Thus, the dT angles represented in FIG. 5B are in general greater; additionally, the dT angles reflects a greater amount of dispersion (e.g., the extent to which individual data points in a dataset vary or spread out from the central tendency). Greater dispersion may be strongly correlated with a risk for SCD and VT / VF events. It should be understood that the techniques of thisdisclosure may use any suitable method of statistical analysis in addition to or alternative to box and whisker plots to evaluate the dispersion of the dT angle.

[0049] FIG. 6 is a block diagram illustrating an example configuration of components of external device 12. In the example of FIG. 6, external device 12 includes processing circuitry 80, communication circuitry 82, storage device 84, and user interface 86.

[0050] Processing circuitry 80 may include one or more processors that are configured to implement functionality and / or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 80.

[0051] Communication circuitry 82 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from, as well as send uplink telemetry to, IMD 10, or another device. Communication circuitry 82 may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of a variety of forms of wired and / or wireless communication and / or network protocols.

[0052] Storage device 84 may be configured to store information within external device 12 during operation. Storage device 84 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 84 includes one or more of a short-term memory or a long-term memory. Storage device 84 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicative of instructions for execution by processing circuitry 80. Storage device 84 may be used by software or applications running on external device 12 to temporarily store information during program execution.

[0053] Data exchanged between external device 12 and IMD 10 may include operational parameters. External device 12 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters and / or export collected data. For example, processing circuitry 80 may transmit an instruction to IMD 10 which requests IMD 10 to export collected data to external device 12. In turn, external device 12 may receive the collected data from IMD 10 and store the collected data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein.

[0054] A user, such as a clinician or the patient, may interact with external device 12 through user interface 86. User interface 86 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 80 may present information related to IMD 10. For example, user interface 86 may display information such as the status of the device, including any warnings or alerts that may require immediate attention. User interface 86 may also present patient data, such as PRD metrics and turbulence metrics described herein. In some examples, user interface 86 may present the data in a way that facilitates the identification of trends or patterns, such as by displaying dT angles graphs, box and whisker plots, etc. User interface 86 may also display or otherwise communicate information regarding therapeutic actions IMD 10 may perform, such as nerve stimulation, high rate pacing, drug intake, providing recommendations to patients (e.g., taking a pill, relaxing, breathing deeply, etc.), providing recommendations to physicians to change medication or modify doses, etc.

[0055] In addition, user interface 86 may include an input mechanism 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 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

[0056] FIG. 7 is a block diagram illustrating an example system that includes an access point 90, a network 92, external computing devices, such as a server 94, and one ormore other computing devices 100A-100N (collectively, “computing devices 100”), which may be coupled to IMD 10 and external device 12 via network 92, in accordance with one or more techniques described herein. In this example, IMD 10 may use communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 90 via a second wireless connection. In the example of FIG. 7, access point 90, external device 12, server 94, and computing devices 100 are interconnected and may communicate with each other through network 92.

[0057] Access point 90 may include a device that connects to network 92 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 90 may be coupled to network 92 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. IMD 10 may be configured to transmit data to access point 90. Access point 90 may then communicate the retrieved data to server 94 via network 92.

[0058] In some cases, server 94 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, server 94 may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 100. One or more aspects of the illustrated system of FIG. 5 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink® Network.

[0059] In some examples, one or more of computing devices 100 may be a tablet or other smart device located with a clinician, by which the clinician may program, receive data from, and / or interrogate IMD 10. For example, the clinician may access data collected by IMD 10 through a computing device 100, such as when a patient is in in between clinician visits, to check on a status of a medical condition or the operation of IMD 10. In some examples, the clinician may enter instructions for a medical intervention for the patient into an application executed by computing device 100, such as based on a status of a patient condition determined by IMD 10, external device 12, server 94. or any combination thereof, or based on other patient data known to the clinician. Device 100 then may transmit the instructions for medical intervention to another of computingdevices 100 located with the patient or a caregiver of the patient. 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 further examples, a computing device 100 may generate an alert to the patient based on a status of a medical condition of the patient, which may enable the patient proactively to seek medical attention prior to receiving instructions for a medical intervention. In this manner, the patient may be empowered to take action, as needed, to address his or her medical status, which may help improve clinical outcomes for the patient.

[0060] In the example illustrated by FIG. 7, server 94 includes a storage device 96, e.g., to store data retrieved from IMD 10, and processing circuitry 98. Although not illustrated in FIG. 7, computing devices 100 may similarly include a storage device and processing circuitry. Processing circuitry 98 may include one or more processors that are configured to implement functionality and / or process instructions for execution within server 94. For example, processing circuitry 98 may be capable of processing instructions stored in memory 96. Processing circuitry 98 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 98 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 98. Processing circuitry 98 of server 94 and / or the processing circuity of computing devices 100 may implement any of the techniques described herein.

[0061] Storage device 96 may include a computer-readable storage medium or computer-readable storage device. In some examples, memory 96 includes one or more of a short-term memory or a long-term memory. Storage device 96 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 96 is used to store data indicative of instructions for execution by processing circuitry 98.

[0062] FIG. 8 is a flow diagram illustrating an example operation for detecting (including predicting) SCD, VT / VF, or another cardiac disorder in accordance with techniques of this disclosure. Although the example operation of FIG. 8 is described as being performed by processing circuitry 50 of IMD 10, some or all of the example operation may be performed by processing circuitry of another device.

[0063] IMD 10, which may be configured to monitor and regulate the electrical activity of the heart, may sense cardiac activity (800). For example, electrodes 51 may detect electrical signals originating from the heart muscles. Sensing circuitry 52 and / or processing circuitry 50 may process the electrical signals in order to generate a digital representation, such as EGM 64. This digital representation may indicate information about the heart’s electrical behavior, including rhythm, amplitude, and timing.

[0064] Processing circuitry 50 may detect PVCs 68 that occur in EGM 64 (802). Processing circuitry 50 may detect PVCs 68 based on the abnormal and distinctive characteristics of PVCs, such as a wider QRS complex and timing irregularities relative to normal beats. In any case, responsive to detecting PVCs 68, processing circuitry 50 may evaluate PRD metrics, such as dT angle, to detect underlying cardiac conditions like an increased risk of arrhythmias (804). That is, processing circuitry 50 may use PRD analysis to assess the impact of PVCs on the heart’s electrical dynamics and overall cardiac function. For example, when PVC 68B happens, processing circuitry 50 may compare the dT angles within some number (e.g., 3, 4, 5, etc.) of beats of PVC 68B to normal (e.g., average) dT angles.

[0065] Additionally or alternatively, processing circuitry 50 may evaluate one or more turbulence metrics, which may include TO and TS (806). In some examples, processing circuitry 50 may calculate TO as:where dTbaseiine is an average of dT values before occurrence of PVC, and dTiater is an average of dT values immediately following the PVC. In some examples, TS may be calculated by identifying the steepest slope of linear regression in, e.g., a series of five consecutive dT values within the first 15 to 20 dT values following a PVC. In other examples, processing circuitry 50 may calculate TO and TS may by analyzing changes in dT values instead of dT values. For example, dT may be substituted with AdT in the above calculations or techniques, where AdT = dTi+i - dT, or AdT = |dTi+i - dTi| .

[0066] In some examples, processing circuitry 50 may evaluate the risk of a SCD and / or VT / VF event and / or predict an occurrence of SCD and / or VT / VF based on the PRD metrics and turbulence metrics. For example, processing circuitry 50 may, at least in part, determine that the risk of SCD and / or VT / VF is relatively high and / or that SCDand / or VT / VF is predicted if one or more of the dT angles associated with the occurrence of PVC 68B exceeds a predetermined threshold. Additionally or alternatively, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted if one or more of the dT angles associated with the occurrence of PVC 68B deviates by a predetermined amount from normal dT angles. Similarly, processing circuitry 50 may, at least in part, determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted if one or more of the turbulence metrics exceeds a corresponding predetermined turbulence threshold.

[0067] Similarly, processing circuitry 50 may analyze turbulence metrics to determine the risk or predict an occurrence of SCD and / or VT / VF. For example, if the TO values are too large, reflecting an inability of the autonomic nervous system to quickly regulate heart rate after PVC, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted. Similarly, if the TS values deviate significantly from expected values or falls outside predetermined ranges, processing circuitry 50 may determine that the risk of SCD and / or VT / VF is relatively high and / or that SCD and / or VT / VF is predicted.

[0068] In some examples, processing circuitry 50 may determine a risk value or score that quantifies the likelihood of patient 4 experiencing SCD and / or VT / VF. A numeric representation may support longitudinal tracking of risk factors and disease progression, enabling timely adjustments to treatment plans. Additionally, a numeric representation may provide a standardized metric for evaluating risk factors, assessing disease burden, and predicting future health outcomes.

[0069] In some examples, processing circuitry 50 may predict the occurrence of SCD and / or VT / VF based on PRD metrics and turbulence metrics. For example, based on the metrics described herein (and any other relevant patient data), processing circuitry 50 may generate real-time risk predictions for patient 4, estimating the likelihood of SCD and / or VT / VF occurring within a specific timeframe, such as the next 24 hours.

[0070] Based on the PRD metrics and turbulence metrics, processing circuitry 50 may cause IMD 10 to perform an action (808). For example, processing circuitry 50 may determine whether to provide an indication of at least one of SCD or VT / VF based on the one or more PRD metrics and the one or more turbulence metrics. In some examples, based on the one or more PRD metrics and the one or more turbulence metrics, IMD 10(e.g., by therapy delivery circuitry 58) can deliver therapies such as electrical stimulation (e.g., via electrodes 51), drug delivery, pacing to regulate heart rhythms, etc. These therapies may be crucial for maintaining the patient's health and well-being, particularly in view of an increased risk of SCD and / or VT / VF, which require emergency treatment.

[0071] For example, based on the one or more PRD metrics and the one or more turbulence metrics, IMD 10 may deliver nerve stimulation therapy to modulate the activity of nerves in the body. Additionally or alternatively, IMD 10 may deliver high rate pacing to override abnormal electrical signals causing the heart to beat too quickly, reducing the risk of complications like stroke or heart failure. Additionally or alternatively, IMD 10 may deliver or otherwise administer a drug to treat various medical conditions.

[0072] In some examples, processing circuitry 50 may generate or otherwise output alerts to notify the patient or a healthcare provider based on the one or more PRD metrics and the one or more turbulence metrics. These alerts may indicate irregular heart rhythms, identify a need for therapy adjustment (including but not limited to altering medication), etc. By promptly alerting patients and clinicians to potential problems, system 2 may serve an important role in proactive healthcare management, allowing for timely intervention and prevention of complications.

[0073] Processing circuitry 50 may also send information to clinicians or healthcare providers for remote monitoring and analysis. Processing circuitry 50 may transmit data, including the determined risk for SCD and / or VT / VF, associated physiological measurements, etc., to monitoring system 450. Clinicians can then remotely access this data, allowing for continuous monitoring of the patient’s condition without the need for frequent in-person visits. This remote monitoring capability may enable early detection of issues, ultimately improving clinical outcomes.

[0074] This disclosure includes various examples, such as the following examples.

[0075] Example 1 : A medical device system includes an implantable medical device configured to sense cardiac electrogram data; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulencemetrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0076] Example 2: The medical device system of example 1, wherein the one or more periodic repolarization dynamics metrics includes a dT angle, and wherein the dT angle constitutes a change in angle in a repolarization vector between two consecutive cardiac cycles.

[0077] Example 3: The medical device system of example 2, wherein the one or more turbulence metrics are based on a change in the dT angle.

[0078] Example 4: The medical device system of any of examples 1 to 3, wherein the processing circuitry is further configured to: determine a vectorcardiography of the cardiac electrogram data representing cardiac activity in three-dimensional space; and determine the one or more periodic repolarization dynamics metrics based on the vectorcardi ography .

[0079] Example 5: The medical device system of any of examples 1 to 4, wherein the one or more cardiac cycles associated with the premature ventricular contraction event occur within a predetermined number of cardiac cycles of the premature ventricular contraction event.

[0080] Example 6: The medical device system of any of examples 1 to 5, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more periodic repolarization dynamics metrics being greater than a corresponding periodic repolarization dynamics threshold.

[0081] Example 7: The medical device system of any of examples 1 to 6, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more turbulence metrics being greater than a corresponding turbulence threshold.

[0082] Example 8: The medical device system of any of examples 1 to 7, wherein the one or more turbulence metrics include one or more of a turbulence onset or a turbulence slope.

[0083] Example 9: The medical device system of example 8, wherein the turbulence metric is based on: an average of one or more dT angles associated with one or more cardiac cycles preceding the premature ventricular contraction event, and an average of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0084] Example 10: The medical device system of example 8 or 9, wherein the turbulence slope is based on a linear regression of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0085] Example 11 : The medical device system of any of examples 1 to 10, wherein the implantable medical device further includes therapy delivery circuitry, and wherein the processing circuitry is configured to cause the therapy delivery circuitry to deliver pacing to a heart of the patient based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0086] Example 12: The medical device system of any of examples 1 to 11, wherein the processing circuitry is configured to output an alert based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0087] Example 13: The medical device system of any of examples 1 to 12, wherein the implantable medical device includes the processing circuitry.

[0088] Example 14: A method includes sensing, by an implantable medical device, cardiac electrogram data; identifying, by processing circuitry, a premature ventricular contraction event within the cardiac electrogram data; determining, by the processing circuitry and for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determining, by the processing circuitry and for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determining, by the processing circuitry, whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0089] Example 15: The method of example 14, wherein the one or more periodic repolarization dynamics metrics includes a dT angle, and wherein the dT angle constitutes a change in angle in a repolarization vector between two consecutive cardiac cycles.

[0090] Example 16: The method of example 15, wherein the one or more turbulence metrics are based on a change in the dT angle.

[0091] Example 17: The method of any of examples 14 to 16, further includes determining, by the processing circuitry, a vectorcardiography of the cardiac electrogram data representing cardiac activity in three-dimensional space; and determining, by the processing circuitry, the one or more dT angles associated with the premature ventricular contraction event based on the vectorcardiography.

[0092] Example 18: The method of any of examples 14 to 17, wherein the one or more cardiac cycles associated with the premature ventricular contraction event occur within a predetermined number of cardiac cycles of the premature ventricular contraction event.

[0093] Example 19: The method of any of examples 14 to 18, wherein providing the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia is based on at least one of the one or more periodic repolarization dynamics metrics being greater than a corresponding periodic repolarization dynamics threshold.

[0094] Example 20: The method of any of examples 14 to 19, wherein providing the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia is based on at least one of the one or more turbulence metrics being greater than a corresponding turbulence threshold.

[0095] Example 21 : The method of any of examples 14 to 20, wherein the one or more turbulence metrics include one or more of a turbulence onset or a turbulence slope.

[0096] Example 22: The method of example 21, wherein the turbulence onset is based on: an average of one or more dT angles associated with one or more cardiac cycles preceding the premature ventricular contraction event, and an average of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0097] Example 23: The method of example 21 or 22, wherein the turbulence slope is based on a linear regression of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0098] Example 24: The method of any of examples 14 to 23, further including delivering, by the implantable medical device, pacing to a heart of the patient based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0099] Example 25: The method of any of examples 14 to 24, further including outputting, by the processing circuitry, an alert based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0100] Example 26: An implantable medical device includes sensing circuitry configured to sense a cardiac electrogram data of the patient; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0101] Example 27: The implantable medical device of example 26, wherein the one or more periodic repolarization dynamics metrics includes a dT angle, and wherein the dT angle constitutes a change in angle in a repolarization vector between two consecutive cardiac cycles.

[0102] Example 28: The implantable medical device of example 27, wherein the one or more turbulence metrics are based on a change in the dT angle.

[0103] Example 29: The implantable medical device of any of examples 26 to 28, wherein the processing circuitry is further configured to: determine a vectorcardiography of the cardiac electrogram data representing cardiac activity in three-dimensional space; and determine the one or more periodic repolarization dynamics metrics based on the vectorcardi ography .

[0104] Example 30: The implantable medical device of any of examples 26 to 29, wherein the one or more cardiac cycles associated with the premature ventricularcontraction event occur within a predetermined number of cardiac cycles of the premature ventricular contraction event.

[0105] Example 31 : The implantable medical device of any of examples 26 to 30, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more periodic repolarization dynamics metrics being greater than a corresponding periodic repolarization dynamics threshold.

[0106] Example 32: The implantable medical device of any of examples 26 to 31, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more turbulence metrics being greater than a corresponding turbulence threshold.

[0107] Example 33: The implantable medical device of any of examples 26 to 32, wherein the one or more turbulence metrics include one or more of a turbulence onset or a turbulence slope.

[0108] Example 34: The implantable medical device of example 33, wherein the turbulence metric is based on: an average of one or more dT angles associated with one or more cardiac cycles preceding the premature ventricular contraction event, and an average of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0109] Example 35: The implantable medical device of example 33 or 34, wherein the turbulence slope is based on a linear regression of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

[0110] Example 36: The implantable medical device of any of examples 26 to 35, wherein the implantable medical device further includes therapy delivery circuitry, and wherein the processing circuitry is configured to cause the therapy delivery circuitry to deliver pacing to a heart of the patient based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0111] Example 37: The implantable medical device of any of examples 26 to 36, wherein the processing circuitry is configured to output an alert based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

[0112] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, stimulators, or other devices. The terms “processor” and “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

[0113] For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions on a computer-readable storage medium such as RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

[0114] In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and / or discrete electrical circuitry, residing in an IMD and / or external programmer.

Claims

CLAIMSWhat is claimed is:

1. A medical device system comprising: an implantable medical device configured to sense cardiac electrogram data; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

2. The medical device system of claim 1, wherein the one or more periodic repolarization dynamics metrics comprises a dT angle, and wherein the dT angle constitutes a change in angle in a repolarization vector between two consecutive cardiac cycles.

3. The medical device system of claim 2, wherein the one or more turbulence metrics are based on a change in the dT angle.

4. The medical device system of any of claims 1 to 3, wherein the processing circuitry is further configured to: determine a vectorcardiography of the cardiac electrogram data representing cardiac activity in three-dimensional space; and determine the one or more periodic repolarization dynamics metrics based on the vectorcardi ography .

5. The medical device system of any of claims 1 to 4, wherein the one or more cardiac cycles associated with the premature ventricular contraction event occur within a predetermined number of cardiac cycles of the premature ventricular contraction event.

6. The medical device system of any of claims 1 to 5, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more periodic repolarization dynamics metrics being greater than a corresponding periodic repolarization dynamics threshold.

7. The medical device system of any of claims 1 to 6, wherein the processing circuitry is configured to provide the indication of the at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on at least one of the one or more turbulence metrics being greater than a corresponding turbulence threshold.

8. The medical device system of any of claims 1 to 7, wherein the one or more turbulence metrics comprise one or more of a turbulence onset or a turbulence slope.

9. The medical device system of claim 8, wherein the turbulence metric is based on: an average of one or more dT angles associated with one or more cardiac cycles preceding the premature ventricular contraction event, and an average of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

10. The medical device system of claim 8 or 9, wherein the turbulence slope is based on a linear regression of one or more dT angles associated with one or more cardiac cycles immediately following the premature ventricular contraction event.

11. The medical device system of any of claims 1 to 10, wherein the implantable medical device further comprises therapy delivery circuitry, and wherein the processing circuitry is configured to cause the therapy delivery circuitry to deliver pacing to a heart ofthe patient based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

12. The medical device system of any of claims 1 to 11, wherein the processing circuitry is configured to output an alert based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

13. The medical device system of any of claims 1 to 12, wherein the implantable medical device comprises the processing circuitry.

14. An implantable medical device comprising: sensing circuitry configured to sense a cardiac electrogram data of the patient; and processing circuitry configured to: identify a premature ventricular contraction event within the cardiac electrogram data; determine, for at least one or more cardiac cycles associated with the premature ventricular contraction event, one or more periodic repolarization dynamics metrics based on the cardiac electrogram data; determine, for at least the one or more cardiac cycles associated with the premature ventricular contraction event, one or more turbulence metrics based on the one or more periodic repolarization dynamics metrics; and determine whether to provide an indication of at least one of sudden cardiac arrest or ventricular tachyarrhythmia based on the one or more periodic repolarization dynamics metrics and the one or more turbulence metrics.

15. The implantable medical device of claim 14, wherein the one or more periodic repolarization dynamics metrics comprises a dT angle, and wherein the dT angle constitutes a change in angle in a repolarization vector between two consecutive cardiac cycles.

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