Classification of the onset of the pause-triggered event

Abstract

The present disclosure relates to techniques for identifying false detections of pause-triggered episodes in cardiac ECG data. In some examples, a medical system is configured to receive a cardiac electrogram of a pause-triggered episode, the cardiac electrogram sensed by a medical device through a plurality of electrodes; determine, based on the cardiac electrogram, whether one or more false pause detection criteria are met, wherein the one or more false pause detection criteria include at least one criterion for a relative flatness of amplitude values of the electrogram in a time interval between a last pre-pause beat and a pause detection time; classify, based on the determination of whether the false pause detection criteria are met, the pause-triggered episode into one of a plurality of classifications; and output, to a user display, an indication of the classification of the pause-triggered episode.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to medical systems, and more particularly, to medical systems configured to detect cardiac arrest episodes based on cardiac electrograms. BACKGROUND

[0002] Some types of medical devices can monitor a patient's cardiac electrogram (EGM) to monitor electrical activity of the patient's heart. A cardiac EGM is an electrical signal sensed by an electrode. In some examples, a medical device monitors a cardiac EGM to detect one or more types of cardiac arrhythmias, such as bradycardia, tachycardia, fibrillation, or cardiac arrest (e.g., caused by a sinus arrest or an AV block, which is referred to herein as a cardiac arrest). SUMMARY

[0003] In addition to signals representative of electrical activity of the heart, a cardiac EGM can contain noise, artifacts, and the like. Additionally, the amplitude of signals representative of electrical activity of the heart within a cardiac EGM can vary over time, e.g., due to movement of the electrode relative to the heart tissue. Noise, artifacts, and signal amplitude variation can interfere with detection of cardiac depolarization, affecting accurate identification of cardiac arrest episodes using a cardiac EGM. This is due in part to one or more heartbeats not being considered in the cardiac EGM when some cardiac depolarizations are not detected.

[0004] Generally, the present disclosure relates to techniques for verifying detection of an arrest episode in a cardiac EGM. The techniques include analyzing at least a portion of cardiac EGM data stored as an arrest trigger episode to determine whether the arrest trigger episode is most likely to be classified as a true arrest (classification) or a false arrest (classification) based on one or more false arrest detection criteria. In some examples, processing circuitry of a medical device / system performs the analysis in response to meeting the arrest trigger episode detection criteria.

[0005] In some examples, the processing circuitry of the medical device / system classifies the asystole-triggered episode as one of a plurality of classifications based on the analysis, which can facilitate prioritization of the episode review process by a qualified medical clinician. For example, if the processing circuitry determines that the asystole-triggered episode is more likely to be a true asystole, the processing circuitry can classify the asystole episode as a high-priority episode in the review process, and if the processing circuitry determines that the asystole-triggered episode is more likely to be a pseudasystole, the processing circuitry can classify the asystole-triggered episode as a normal-priority classification or a low-priority classification in the review process. The processing circuitry can utilize one or more pseudasystole episode detection criteria to determine whether a pseudasystole-triggered episode is likely to be a pseudasystole and classify it as a low-priority episode or a normal low-priority episode. At least one example criterion evaluates an amplitude value to determine a relative flatness of the episode. In this way, the techniques of the present disclosure can advantageously achieve improved accuracy of identification of true asystoles, improve efficiency of asystole episode review, and thus achieve a better assessment of a patient’s condition.

[0006] In one example, a medical system includes processing circuitry configured to receive a cardiac electrogram of an asystole-triggered episode, the cardiac electrogram sensed by a medical device through a plurality of electrodes; determine, based on the cardiac electrogram, whether one or more pseudasystole detection criteria are satisfied, wherein the one or more pseudasystole detection criteria include at least one criterion for a relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pre-asystole beat and an asystole detection time; classify the asystole-triggered episode as one of a plurality of classifications based on the determination of whether the pseudasystole detection criteria are satisfied; and output an indication of the classification of the asystole-triggered episode to a user display.

[0007] In another example, a method includes receiving a cardiac electrogram of an asystole-triggered episode, the cardiac electrogram sensed by a medical device through a plurality of electrodes; determining, based on the cardiac electrogram, whether one or more pseudasystole detection criteria are satisfied, wherein the one or more pseudasystole detection criteria include at least one criterion for a relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pre-asystole beat and an asystole detection time; classifying the asystole-triggered episode as one of a plurality of classifications based on the determination of whether the pseudasystole detection criteria are satisfied; and outputting an indication of the classification of the asystole-triggered episode to a user display.

[0008] In another example, a non-transitory computer-readable storage medium comprises program instructions that, when executed by processing circuitry of a medical system, cause the processing circuitry to receive a cardiac electrogram of a pace termination episode, the cardiac electrogram sensed by a medical device through a plurality of electrodes; determine, based on the cardiac electrogram, whether one or more false pace detection criteria are met, wherein the one or more false pace detection criteria comprise at least one criterion for a relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pace and a pace detection time; classify, based on the determination of whether the false pace detection criteria are met, the pace termination episode into one of a plurality of classifications; and output an indication of the classification of the pace termination episode to a user display.

[0009] 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 systems, devices, and methods described in detail in the accompanying drawings and the specific embodiments below. Further details, aspects, and advantages of one or more examples of the present disclosure are set forth in the detailed description and drawings included below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF DRAWINGS

[0010] FIG. 1 Environments for example medical systems are illustrated in connection with patients.

[0011] FIG. 2 is a functional block diagram of an example configuration of an implantable medical device (IMD) of the medical system of FIG. 1

[0012] is a conceptual side view of an example configuration of the IMD of FIG. 3 and FIG. 1 FIG. 2

[0013] FIG. 4 is a functional block diagram of an example configuration of an external device of the medical system of FIG. 1

[0014] is a block diagram illustrating an example system including an access point, a network, an external computing device, such as a server, and one or more other computing devices that can be coupled to the IMD and external device of FIG. 5 FIGS. 1-4

[0015] FIG. 6 is a flowchart illustrating example operations for determining whether an identification of a pace episode is false based on whether a plurality of false pace detection criteria are met.

[0016] FIG. 7 ​​​​is a flowchart showing example operations for determining whether an identification of a cardiac arrest episode is spurious based on whether a plurality of spurious asystole detection criteria are met.

[0017] FIGS. 8A-8B is an illustration of a cardiac electrogram. FIG. 8A Example cardiac electrograms showing a true arrest episode, and FIG. 8B Example cardiac electrograms showing a spurious arrest episode.

[0018] FIGS. 9A-9B are illustrations of cardiac electrograms. FIG. 9A Example cardiac electrograms showing a true arrest episode and a time interval of a spurious arrest detection criterion. FIG. 9B Example cardiac electrograms showing a spurious arrest episode and a time interval of a spurious arrest detection criterion.

[0019] FIG. 10A is a conceptual diagram showing a front view of a patient with another example medical system.

[0020] FIG. 10B is a conceptual diagram showing a side view of a patient with FIG. 10A an example medical system.

[0021] FIG. 10C is a conceptual diagram showing a lateral view of a patient with FIG. 10A an example medical system.

[0022] Throughout the specification and drawings like reference numerals refer to like elements. DETAILED DESCRIPTION

[0023] A variety of types of medical devices sense cardiac EGMs. Some medical devices that sense cardiac EGMs are non-invasive, for example using a plurality of electrodes placed in contact with an external portion of a patient, such as at various locations on the patient's skin. As an example, electrodes used in these non-invasive procedures for monitoring cardiac EGMs can be attached to a patient using adhesive, a belt, a waistband, or a vest, and electrically coupled to a monitoring device, such as an electrocardiograph, a Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with electrical activity of a patient's heart or other cardiac tissue, and provide these sensed electrical signals to the electronic device for further processing and / or display of the electrical signals. Non-invasive devices and methods can be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a physician's appointment, or for example over a scheduled period of time, such as a day (twenty-four hours), or a period of several days.

[0024] External devices that can be used for non-invasive sensing and monitoring of cardiac EGMs include wearable devices, such as patches, watches, or necklaces, that have electrodes configured to contact the skin of a patient. One example of a wearable physiological monitor configured to sense a cardiac EGM is the SEEQ® patch monitor commercially available from Medtronic plc of Dublin, Ireland. TM Mobile cardiac telemetry systems. Such external devices can facilitate relatively long-term monitoring of a patient during normal daily activities, and can periodically transmit collected data to a network service, such as the Carelink® Network of Medtronic plc. TM

[0025] Implantable medical devices (IMDs) also sense and monitor cardiac EGMs. Electrodes used by IMDs to sense cardiac EGMs are often integrated with the housing of the IMD and / or coupled to the IMD through one or more elongated leads. Example IMDs that monitor cardiac EGMs include pacemakers and implantable cardioverter-defibrillators, which can be coupled to intravascular or extravascular leads, as well as pacemakers having housings configured for implantation within the heart, which can be leadless. One example of a pacemaker configured for intracardiac implantation is the Micra® LVX® pacemaker commercially available from Medtronic plc. TM Transcatheter pacing systems. Some IMDs that do not provide therapy, such as implantable patient monitors, can sense cardiac EGMs. One example of such an IMD is the Reveal LINQ® insertable cardiac monitor commercially available from Medtronic plc. TM Insertable cardiac monitors, which can be inserted subcutaneously. Such IMDs can facilitate relatively long-term monitoring of a patient during normal daily activities, and can periodically transmit collected data to a network service, such as the Carelink® Network of Medtronic plc. TM

[0026] Regardless of the type of device used, noise signals, which can be referred to as artifacts, can appear in the cardiac EGM and interfere with the sensing of a de-polarization, such that one or more normal beats are not detected by the medical device and lost from the cardiac EGM. The duration of a noise signal can extend over a portion of the normal timeframe of a cardiac cycle, or can extend over a time span of multiple cardiac cycles that can be expected to have occurred. Such noise signals can be more prevalent when using skin, subcutaneous, or extravascular electrodes to sense the cardiac EGM, for example, due to temporary changes in contact between at least one of the electrodes and the tissue in which the electrode is positioned due to relative motion of the electrode and tissue. In some examples, the noise signal appears as a drift in the baseline of the cardiac EGM, and can include a portion that decays back toward a steady-state baseline.

[0027] ​​The presence of noise signals, artifacts, or undetected beats in sensed cardiac EGMs can lead to the detection of false pause trigger episodes by the circuitry. In addition, the amplitude of the cardiac signal within a sensed cardiac EGM can vary over time, for example, due to respiration. Such cardiac signal amplitude variations can also be more prevalent in cardiac EGMs sensed using skin, subcutaneous, or extravascular electrodes. Cardiac signal amplitude variations can also lead to the detection of pause episodes that ultimately are false pause episodes.

[0028] False pause trigger episodes can be inserted into the queue for manual review (e.g., by a clinician), increasing the burden of episode review. These types of variations in cardiac signal amplitude can lead to incorrect analysis of the actual cardiac activity occurring in a patient being monitored, for example, by triggering false positive indications of a cardiac event (such as a cardiac pause) that did not actually occur in the patient. Such false positive indications can lead to incorrect assessments of the patient’s condition, including providing therapy and / or sending false alarms to medical personnel responsible for caring for the monitored patient. Low pass filtering of the cardiac EGM generally does not help address these issues, as these types of noise signals and amplitude variations can occur at frequencies near or below the frequency of the cardiac signal.

[0029] Medical systems / devices according to the present disclosure implement techniques for identifying false pause trigger episode detections in a cardiac EGM by, for example, detecting the presence of noise signals, artifacts, undetected beats, and cardiac signal amplitude variations. In some examples, processing circuitry of the system analyzes the cardiac EGM related to an identified pause episode to determine whether one or more of a plurality of false pause detection criteria are met. Each false pause detection criterion can be configured to detect one or more indicators of noise, artifacts, undetected beats, and / or amplitude variations in the cardiac EGM. These indicators are found to be predictors of false pauses, and are based on features corresponding to normal beat counts, noise status of the last pre-pause beat, and relative flatness of the EGM signal in the episode portion that meets one or more pause detection criteria or is otherwise identified as a pause by the medical device, referred to as a pause interval, such as the pause interval between the last pre-pause beat and the pause detection point.

[0030] In some examples, the processing circuitry of the medical system / device performs this analysis in response to a cardiac EGM received from another device (e.g., after an initial asystole detection criterion is met). The medical system / device can determine whether to designate the cardiac EGM as a high priority, normal priority, or low priority analysis based on this analysis. In some examples, in response to determining that none of the one or more asystole episode detection criteria are met, the processing circuitry of the medical system classifies the episode as a true asystole episode of high priority. On the other hand, if at least one false asystole episode detection criterion is determined to be met, the processing circuitry of the medical system classifies the episode as a false asystole trigger episode. Depending on which of the one or more false asystole trigger episode detection criteria are met, the processing circuitry can further classify the false asystole episode as low priority or normal priority. After applying at least one false asystole detection criterion to determine whether the asystole episode is a true asystole episode or a false asystole episode, at least one additional criterion can be employed to classify the false asystole trigger episode as low priority or normal priority.

[0031] The processing circuitry can perform the techniques of the present disclosure in response to detection of an asystole, or substantially in real-time during review of cardiac EGM data for episodes identified as asystoles at a later time. In either case, the processing circuitry can include processing circuitry of the medical device that detected the asystole and / or processing circuitry of another device, such as a local or remote computing device that retrieves episode data from the medical device. In this way, the techniques of the present disclosure can advantageously enable improved accuracy of identification of true asystoles, and thus better assessment of a patient’s condition.

[0032] Some examples below disclose a medical system and techniques for prioritizing cardiac EGM data during review of asystole electrogram episodes following initial detection. One example discloses at least one medical system and technique for classifying asystole-triggered electrogram episodes as either “true asystole of high priority” or “false asystole,” and then classifying false asystole-triggered electrogram episodes as either “false asystole of normal priority” or “false asystole of low priority,” and placing the cardiac EGM data in respective review queues such that cardiac EGM data placed in a high priority queue is reviewed first. For example, cardiac EGM data is placed in a high priority queue based on a number of beats in the episode, a noise status of a beat immediately preceding the asystole, and a relative flatness of the signal within the asystole interval. Specifically, if a total number of beats in the episode window is above a threshold, a beat recorded in the second immediately preceding the asystole interval is not a noise beat, and the signal is relatively flat within the asystole interval of the episode window, the medical system and techniques flag the cardiac EGM data as high priority.

[0033] To define relative flatness within a pause interval, medical systems and techniques can analyze one or more amplitude criteria, such as a criterion requiring a positive final difference between the amplitude difference over another time interval (e.g., the difference between the maximum and minimum amplitudes) and the amplitude difference within the pause interval. Another criterion can indicate the minimum number of samples that cross an amplitude threshold (e.g., within the pause interval). As an example, medical systems and techniques will mark cardiac EGM data as low priority if any of the following conditions are met: 1) the number of normal beats within a 45-second attack window is less than 20; 2) the number of samples crossing the threshold within a 2.5-second pause interval is greater than 2, and the beats detected by the medical system and techniques before the pause interval are noisy beats; 3) the number of normal beats within a 45-second attack window is less than 30, and the final difference is less than 2000; or 4) the final difference is less than -600.

[0034] FIG. 1 An example medical system 2 for patient 4 is illustrated, combining one or more technologies according to this disclosure. The example technologies can be used with an IMD 10, which can be used with an external device 12 and... FIG. 1 At least one of the other devices not shown in the diagram communicates wirelessly. In some examples, the IMD 10 can be implanted outside the chest cavity of patient 4 (e.g., subcutaneously). FIG. 1 (As described in the pectoral muscle location). IMD 10 can be positioned near the sternum at or just below the patient's heart level, for example, at least partially within the heart contour. IMD 10 contains multiple electrodes ( FIG. 1 (Not shown in the image), and is configured to sense cardiac EGM via multiple electrodes. In some examples, the IMD 10 employs LINQ. TM In the form of ICM.

[0035] External device 12 may be a computing device having a user-viewable display and an interface (i.e., a user input mechanism) for providing input to external device 12. In some examples, external device 12 may be a laptop computer, tablet computer, workstation, one or more servers, cellular phone, personal digital assistant, or another computing device that can run an application that enables the computing device to interact with IMD 10.

[0036] External device 12 is configured to communicate wirelessly with IMD 10 and optionally with another computing device. FIG. 1 (Not specified in the text) Communication. For example, external device 12 can communicate via near-field communication technology (e.g., inductive coupling, NFC, or other communication technology that can operate at a range of less than 10-20 cm) and far-field communication technology (e.g., according to 802.11 or The standard set of radio frequency (RF) telemetry or other communication technologies that can operate at a range greater than that of near-field communication technologies can be used for communication.

[0037] External device 12 can be used to configure the operating parameters of IMD 10. External device 12 can be used to retrieve data from IMD 10. The retrieved data may include values ​​of physiological parameters measured by IMD 10, indications of arrhythmias or other disease episodes detected by IMD 10, and physiological signals recorded by IMD 10. For example, external device 12 can retrieve cardiac EGM segments recorded by IMD 10, since IMD 10 determines that a cardiac arrest or another disease episode occurred during said segment. The following will discuss... FIG. 5 In more detail, one or more remote computing devices may interact with IMD 10 via a network in a manner similar to external device 12, for example, to program IMD 10 and / or retrieve data from IMD 10.

[0038] The processing circuitry of medical system 2, such as the IMD 10, external device 12, and / or one or more other computing devices, can be configured to perform the example techniques of this disclosure for identifying false cardiac arrest detection. In some examples, the processing circuitry of medical system 2 analyzes the cardiac EGM sensed by IMD 10 and associated with the identified cardiac arrest episode to determine whether one or more of a plurality of false cardiac arrest detection criteria are met. Each of the false cardiac arrest detection criteria can be configured to detect one or more indicators of noise, artifacts, and / or amplitude variations in the cardiac EGM. Although described in the context of examples in which the IMD 10 sensing the cardiac EGM includes an insertable cardiac monitor, example systems comprising one or more implantable or external devices of any type configured to sense the cardiac EGM can be configured to implement the techniques of this disclosure.

[0039] FIG. 2 This demonstrates one or more technologies described herein. FIG. 1 A functional block diagram of an example configuration of IMD 10 is provided. In the illustrated example, IMD 10 includes electrodes 16A and 16B (collectively referred to as “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, and sensor 62. Although the illustrated example includes two electrodes 16, in some examples, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure.

[0040] Processing circuitry 50 can include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 can 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 can 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 can be embodied as software, firmware, hardware or any combination thereof.

[0041] Sensing circuitry 52 can be selectively coupled to electrodes 16 through switching circuitry 58, e.g., to select electrodes 16 and polarities for sensing a cardiac EGM, referred to as a sensing vector, as controlled by processing circuitry 50. Sensing circuitry 52 can sense signals from electrodes 16, e.g., to produce a cardiac EGM, to facilitate monitoring of electrical activity of the heart. As an example, sensing circuitry 52 can also monitor signals from sensors 62, which can include one or more accelerometers, pressure sensors, and / or optical sensors. In some examples, sensing circuitry 52 can include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and / or sensors 62.

[0042] Sensing circuitry 52 and / or processing circuitry 50 can be configured to detect a cardiac depolarization (e.g., a P-wave or an R-wave) when a cardiac EGM amplitude crosses a sensing threshold. In some examples, the sensing threshold can be automatically adjusted over time using any of a variety of automatic sensing threshold adjustment techniques known in the art. For example, in response to detection of a cardiac depolarization, the sensing threshold used to detect subsequent cardiac depolarizations can decay from an initial value over a period of time. Sensing circuitry 52 and / or processing circuitry 50 can determine the initial value based on the amplitude of the detected cardiac depolarization. The initial value and decay of the adjustable sensing threshold can be configured such that the sensing threshold is relatively high shortly after a detected cardiac depolarization when a subsequent depolarization is not expected, and decays to a relatively lower value over time as the occurrence of a cardiac depolarization becomes more likely. In some examples, sensing circuitry 52 can include a rectifier, a filter, an amplifier, a comparator, and / or an analog-to-digital converter for cardiac depolarization detection.

[0043] In some examples, sensing circuitry 52 can output an indication to processing circuitry 50 in response to sensing a cardiac depolarization. In this way, processing circuitry 50 can receive detected cardiac depolarization indicators corresponding to occurrences of detected R-waves and P-waves in respective chambers of the heart. Processing circuitry 50 can use the indications of detected R-waves and P-waves to determine a heart rate and to detect cardiac arrhythmias, such as tachyarrhythmias and asystole.

[0044] Communication circuitry 54 can 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 can receive downlink telemetry from and send uplink telemetry to external device 12 or another device, with the aid of, for example, an internal or external antenna 26. In addition, processing circuitry 50 can process received data packets and / or commands, and fetch and / or refresh data from memory 56 for transmission. A computer network, such as a network or the Internet, communicates with networked computing devices. Antenna 26 and communication circuitry 54 can be configured to transmit and / or receive signals through inductive coupling, electromagnetic coupling, near-field communication (NFC), radio frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0045] In some examples, storage 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 56 can 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. As examples, storage 56 can store 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 56 and transmitted by communication circuitry 54 to one or more other devices can include suspected or potential pacing-trigger episode data and / or an indication that a suspected or potential pacing-trigger episode meets one or more false pacing-trigger episode detection criteria.

[0046] Processing circuitry 50 can detect a pause-triggered episode based on determining that the cardiac electrogram satisfies the initial pause-triggered episode detection criteria. The presence of a pause in the cardiac EGM signal (or the absence of a cardiac depolarization without a recorded normal beat for a predetermined period of time, e.g., three seconds) results in the initial detection of a pause-triggered episode. In such examples, processing circuitry 50 can determine that the cardiac EGM satisfies the initial pause-triggered episode detection criteria based on reaching the predetermined period of time from the detection of a cardiac depolarization without receiving another cardiac depolarization indication from sensing circuitry 52.

[0047] Sensing circuitry 52 can also provide one or more digitized cardiac EGM signals to processing circuitry 50 for analysis, e.g., for cardiac rhythm discrimination, and / or for analysis to determine whether the initial pause-triggered episode detection criteria are satisfied. In some examples, based on the satisfaction of the initial pause-triggered episode detection criteria, processing circuitry 50 can store a digitized cardiac EGM segment corresponding to the suspected pause-triggered episode as episode data in storage 56. The digitized cardiac EGM segment can contain samples of the cardiac EGM across the period of time in which sensing circuitry 52 did not indicate the detection of a depolarization and a period of time before and / or after this period of time in which a depolarization was detected. In accordance with the techniques of this disclosure, processing circuitry 50 of IMD 10 and / or processing circuitry of another device that retrieves the episode data from IMD 10 can analyze the cardiac EGM segment to determine whether one or more pseudo-asystole detection criteria are satisfied.

[0048] Sensing circuitry 52 can also provide one or more digitized cardiac EGM signals to external device 12 for real-time or offline analysis, e.g., for cardiac rhythm discrimination, and / or for real-time or offline analysis to determine whether one or more pseudo-asystole detection criteria are satisfied in accordance with the techniques of this disclosure. In accordance with the techniques of this disclosure, sensing circuitry 52 can also provide cardiac EGM data to external device 12 for real-time or offline analysis, e.g., for cardiac rhythm discrimination, and / or for real-time or offline analysis to determine whether one or more pseudo-asystole detection criteria are satisfied. As an alternative, sensing circuitry 52 can also provide one or more digitized cardiac EGM signals to processing circuitry 50 for analysis, e.g., for cardiac rhythm discrimination, and / or for analysis to determine whether one or more pseudo-pause-triggered episode detection criteria are satisfied.

[0049] In accordance with the techniques of this disclosure, communication circuitry 54 can provide cardiac EGM data to external device 12 for real-time or offline analysis, e.g., for cardiac rhythm discrimination, and / or for real-time or offline analysis to determine whether one or more pseudo-asystole detection criteria are satisfied.

[0050] FIG. 3 is illustrated FIG. 1 and FIG. 2 conceptual side view of an example configuration of IMD 10. In FIG. 3 the example shown, IMD 10 can include a leadless subcutaneous monitoring device having a housing 15 and an insulating cover 76. Electrodes 16A and 16B can be formed or placed on an outer surface of cover 76. Circuitry 50-62 described above with respect to FIG. 2 may be formed or placed on an inner surface of cover 76 or within housing 15. In the example shown, antenna 26 is formed or placed on an inner surface of cover 76, but in some examples, can be formed or placed on an outer surface. In some examples, insulating cover 76 can be positioned over open housing 15 such that housing 15 and cover 76 enclose antenna 26 and circuitry 50-62 and protect the antenna and circuitry from fluids, such as body fluids.

[0051] One or more of antenna 26 or circuitry 50-62 can be formed on an inner side of insulating cover 76, such as by using flip-chip technology. Insulating cover 76 can be flipped onto housing 15. When flipped and placed onto housing 15, components of IMD 10 formed on the inner side of insulating cover 76 can be positioned in a gap 78 defined by housing 15. Electrodes 16 can be electrically connected to switching circuitry 58 through one or more through-holes (not shown) formed through insulating cover 76. Insulating cover 76 can be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Housing 15 can be formed of titanium or any other suitable material, such as a biocompatible material. Electrodes 16 can be formed of any of stainless steel, titanium, platinum, iridium, or alloys thereof. Additionally, electrodes 16 can be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes can be used.

[0052] FIG. 4 is a block diagram illustrating an example configuration of components of external device 12. In FIG. 4 example, external device 12 includes processing circuitry 80, communication circuitry 82, storage 84, and user interface 86.

[0053] The processing circuitry 80 can include one or more processors configured to execute the functions and / or process instructions for execution within the external device 12. For example, the processing circuitry 80 can be capable of processing instructions stored in the storage device 84. The processing circuitry 80 can include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or combinations of any of the foregoing. Accordingly, the processing circuitry 80 can include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions of the processing circuitry 80 described herein.

[0054] The communication circuitry 82 can include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as the IMD 10. Under the control of the processing circuitry 80, the communication circuitry 82 can receive downlink telemetry from, as well as send uplink telemetry to, the IMD 10 or another device. The communication circuitry 82 can be configured to transmit and / or receive signals through inductive coupling, electromagnetic coupling, NFC, RF communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes. The communication circuitry 82 can also be configured to communicate with devices other than the IMD 10 through any of a variety of forms of wired and / or wireless communication and / or network protocols.

[0055] The storage device 84 can be configured to store information during operation of the external device 12. The storage device 84 can include a computer-readable storage medium or computer-readable storage device. In some examples, the storage device 84 includes one or more of a short-term memory or a long-term memory. The storage device 84 can include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memory, or various forms of EPROM or EEPROM. In some examples, the storage device 84 is used to store data indicative of instructions executed by the processing circuitry 80. The storage device 84 can be used by software or applications running on the external device 12 to temporarily store information during program execution.

[0056] Data exchanged between external device 12 and IMD 10 can include operational parameters. External device 12 can transmit data including computer-readable instructions that, when implemented by IMD 10, can control IMD 10 to change one or more operational parameters and / or to export collected data. For example, processing circuitry 80 can transmit instructions to IMD 10 requesting that IMD 10 export collected data (e.g., cardiac arrest episode data) to external device 12. In turn, external device 12 can receive the collected data from IMD 10 and store the collected data in storage 84. Processing circuitry 80 can implement any of the techniques described herein to analyze cardiac EGMs received from IMD 10, for example, to determine whether one or more pseudo-pause-triggered episode detection criteria are met.

[0057] A user, such as a clinician or patient 4, can 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 light emitting diode (LED) display, or other type of screen, where the processing circuitry 80 can present information related to IMD 10, such as a cardiac EGM, a detection indication of an episode of cardiac arrhythmia, and a determination indication of whether one or more pseudo-pause-triggered episode detection criteria are met. In addition, user interface 86 can include input mechanisms configured to receive input from a user. Input mechanisms can include any one or more of, for example, buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that permits a user to navigate through a user interface 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 a user, receiving voice commands from a user, or both.

[0058] Between external device 12 and IMD 10, the IMD can send cardiac EGM data that records a cardiac EGM signal, and external device 12 can receive the cardiac EGM data and classify the cardiac EGM signal into a plurality of categories. Processing circuitry 80 can apply one or more pseudo-pause-triggered episode detection criteria to determine whether a pseudo-pause-triggered episode is a pseudo-pause or a real pause, and then classify the pseudo-pause or real pause as a low priority, normal priority, or high priority episode for review. In some examples, the one or more pseudo-pause-triggered episode detection criteria include at least one amplitude criterion configured to assess a relative flatness of at least a portion of the pause-triggered episode. The cardiac EGM signal is indicative of electrical activity of the heart, and during a pause interval, the amplitude values remain relatively flat in a real pause but significantly vary in a pseudo-pause.

[0059] In some examples, processing circuitry 80 of external device 12 determines, based on the analysis, whether to provide or withhold an indication that the patient experienced a true asystole-triggered episode (or simply a triggered episode) (e.g., to a clinician or other user). In some examples, processing circuitry 80 of external device determines, based on the analysis, whether to provide or withhold an indication that the patient experienced a false asystole-triggered episode (or simply a triggered episode) (e.g., to a clinician or other user).

[0060] FIG. 5 is a block diagram illustrating an example system including an access point 90, a network 92, an external computing device such as a server 94, and one or more other computing devices 100A-100N (collectively, “computing devices 100”) that can be coupled with IMD 10 and external device 12 through network 92, in accordance with one or more techniques described herein. In this example, IMD 10 can use communication circuitry 54 to communicate with external device 12 through a first wireless connection and with access point 90 through a second wireless connection. In this example, external device 12 can use communication circuitry 82 to communicate with access point 90 through a third wireless connection. In this example, server 94 and computing devices 100 can be coupled with access point 90 through network 92. FIG. 5 In the example of FIG. 1, access point 90, external device 12, server 94, and computing devices 100 are interconnected and can communicate with each other through network 92.

[0061] Access point 90 can include a device connected to network 92 through any of a variety of connections including telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 90 can be coupled to network 92 through different forms of connections, including wired connections or wireless connections. In some examples, access point 90 can be a user device, such as a tablet computer or smartphone, that can be co-located with the patient. IMD 10 can be configured to transmit data, such as asystole episode data and indications of satisfaction of one or more false asystole detection criteria, to access point 90. Access point 90 can then communicate the retrieved data to server 94 via network 92.

[0062] In some cases, server 94 can 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 can assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, through computing devices 100. FIG. 5 One or more aspects of the illustrated system can be implemented with general network technologies and functions similar to those provided by Medtronic CareLink® Network. The general network technologies and functions provided by Medtronic CareLink® Network.

[0063] In some examples, one or more computing devices in computing device 100 may be tablets or other smart devices located with the clinician, through which the clinician may be programmed to receive alerts and / or query IMD 10. For example, the clinician may access data collected by IMD 10, such as when patient 4 is between clinician visits, via computing device 100 to check the status of the patient's medical condition. In some examples, the clinician may input instructions for medical interventions for patient 4 into an application executed by computing device 100, such as based on the status of the patient's 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 may then transmit the instructions for medical interventions to another computing device in computing device 100 located with patient 4 or patient 4's caregiver. For example, such instructions for medical interventions may include instructions to change medication dosage, timing, or selection, instructions to schedule clinician visits, or instructions to seek medical care. In another example, computing device 100 can generate alerts to patient 4 based on the status of patient 4's medical condition, enabling patient 4 to proactively seek medical attention before receiving instructions for medical intervention. In this way, patient 4 can be authorized to take action as needed to address his or her medical condition, which can help improve patient 4's clinical outcomes.

[0064] In the FIG. 5 In the example shown, server 94 includes, for example, a storage device 96 and a processing circuitry 98 for storing data retrieved from IMD 10. Although FIG. 5 Unless otherwise specified, computing device 100 may similarly include storage devices and processing circuitry. Processing circuitry 98 may include one or more processors configured to implement functions and / or processing instructions for execution within server 94. For example, processing circuitry 98 may be able to process instructions stored in storage device 96. Processing circuitry 98 may include, for example, a microprocessor, DSP, ASIC, FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Therefore, processing circuitry 98 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of processing circuitry 98 as described herein. The processing circuitry 98 of server 94 and / or the processing circuitry of computing device 100 may implement any of the techniques described herein to analyze cardiac EGM received from IMD 10, for example, to determine whether arrest-triggered episodes and one or more pseudo-arrest-triggered episode detection criteria are met.

[0065] Storage 96 can include computer-readable storage media or computer-readable storage devices. In some examples, storage 96 includes one or more of short term memory or long term memory. Storage 96 can include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memory, or various forms of EPROM or EEPROM. In some examples, storage 96 is used to store data indicative of instructions executed by processing circuitry 98.

[0066] FIG. 6 is a flowchart illustrating example operations for determining whether an identification of a pacing trigger episode is spurious based on whether one or more spurious pacing trigger episode detection criteria are met. According to FIGS. 1-5 Medical system 2 of the illustrated example includes various computing hardware / software components, some of which are medical devices communicably coupled to one another by a communication protocol.

[0067] In one example, processing circuitry 50 of example medical device IMD 10 determines that at least one pacing trigger episode detection criterion is met based on a cardiac EGM sensed by sensing circuitry 52 of IMD 10. For example, as described with respect to FIG. 2 As discussed in greater detail, processing circuitry 50 can determine that a threshold time interval, e.g., 2-3 seconds, has elapsed since sensing circuitry 52 identified a cardiac depolarization, e.g., an R-wave, within the cardiac EGM.

[0068] External device 12 (another example medical device of medical system 2) includes processing circuitry 80 to receive the cardiac EGM and evaluate various respective data sets by applying one or more spurious pacing trigger episode detection criteria to those data sets (120). As described herein, example spurious pacing trigger episode detection criteria can include one or more conditions that, if met, indicate a likely spurious pacing trigger episode. If none of the one or more spurious pacing trigger episode detection criteria are met, the pacing trigger episode is most likely a true pace. In some cases, medical system 2 classifies the pacing trigger episode for use in, e.g., the review process depicted. FIG. 7

[0069] To properly evaluate the cardiac EGM for false positives, medical system 2 can apply three spurious pacing trigger episode detection criteria to the cardiac EGM, as described with respect to FIG. 6 ​is shown. In the illustrated example, processing circuitry 80 determines whether an example pseudo pause-triggered episode detection criterion comprising a normal beat count criterion is satisfied (122). The normal beat count criterion can indicate that there are not enough normal beats in the pause-triggered episode. For example, processing circuitry 80 can determine whether a threshold number of normal beats in a predetermined time period retrogressing from the most recent time that the pause-triggered episode detection criterion was satisfied satisfies the pseudo pause-triggered episode detection criterion. In some examples, the episode includes cardiac EGM data for a time period before and after the interval identified as a pause, and processing circuitry 80 can determine whether a threshold number of normal beats of the episode data other than the interval identified as a pause satisfies the pseudo pause-triggered episode detection criterion.

[0070] One example threshold number of normal beats is a fixed predetermined number, while another example threshold can be determined as a ratio or percentage of normal beat counts to total beat counts over the predetermined time period described above; for example, if the normal beat count is less than eighty percent (80%) of the total beat count over the predetermined time period, processing circuitry 80 can identify the pause-triggered episode as a pseudo pause. This implementation of the normal beat count criterion is based on the observation that pseudo detection of pause-triggered episodes tends to occur when there is considerable noise present, such as when there are not enough normal beats. Noise signals can intermittently or at varying frequencies appear in the cardiac EGM based on changes in, for example, the condition of IMD 10 or patient 4. Thus, there is a greater likelihood that a recently detected pause is actually pseudo (e.g., caused by noise) during a time period in which there are not enough detected normal beats (indicating that there can be noise in the EGM) as compared to a time period in which there are enough numbers of detected normal beats. As in the example operation of FIG. 2, requiring satisfaction of the normal beat count criterion can avoid classifying a suspect pause-triggered episode as a true pause by the pseudo pause-triggered episode detection criterion. FIG. 6

[0071] In some examples, processing circuitry 80 analyzes the morphology of the cardiac EGM signal for each beat detected within the cardiac EGM or a portion thereof. Processing circuitry 80 can classify each beat as normal or another classification based on the analysis. The morphology analysis can include comparing the cardiac EGM signal for each beat to one or more templates (e.g., a normal beat template).

[0072] In response to determining that the normal beat count criterion is not satisfied (the “NO” branch of 122), processing circuitry 80 continues to apply a second of the three pseudo pause-triggered episode detection criteria. If the normal beat count criterion is satisfied (the “YES” branch of 122), the pause-triggered episode is likely to be a pseudo pause, and processing circuitry 80 can apply a third of the three pseudo pause-triggered episode detection criteria. FIG. 6 ​The example operations of FIG. 12 end (130). In the illustrated example, processing circuitry 80 determines whether a second pseudo asystole trigger onset detection criterion comprising a noise state criterion is met (124). The noise state criterion can indicate whether a last pre-asystole beat in the cardiac EGM is noisy, e.g., whether a morphology classification of the beat in the cardiac EGM prior to the interval identified as asystole is noisy or otherwise abnormal.

[0073] In response to determining that the noise state criterion is not met (the "NO" branch of 124), processing circuitry 80 continues to apply at least a third pseudo asystole trigger onset detection criterion of the three pseudo asystole trigger onset detection criteria. Based on whether the noise state criterion is met (the "YES" branch of 124), the asystole trigger onset is likely to be a pseudo asystole, and FIG. 6 The example operations of FIG. 12 end (130). In the illustrated example, processing circuitry 80 determines whether a second pseudo asystole trigger onset detection criterion comprising a noise state criterion is met (124). The noise state criterion can indicate whether a last pre-asystole beat in the cardiac EGM is noisy, e.g., whether a morphology classification of the beat in the cardiac EGM prior to the interval identified as asystole is noisy or otherwise abnormal. FIGS. 8A-8B and FIGS. 9A-9B The criteria for determining relative flatness are described in more detail.

[0074] An example amplitude criterion is satisfied if the non-positive difference between the comparison window maximum-minimum amplitude difference value and the candidate window maximum-minimum amplitude difference value. The candidate window maximum-minimum amplitude difference value refers to the difference between the maximum amplitude and the minimum amplitude within the time interval containing the asystole (i.e., the asystole interval). The comparison window maximum-minimum amplitude difference value refers to the difference between the maximum amplitude and the minimum amplitude within another time interval in the same cardiac EGM containing the onset of normal beats. Alternative example amplitude criteria include a threshold difference of less than zero (0) or greater than twenty thousand (20,000) for the difference between the maximum-minimum amplitude difference values between the comparison window and the candidate window. If the example amplitude criterion is met (the "YES" branch of 126), the asystole trigger onset is likely to be a pseudo asystole.

[0075] Another example amplitude criterion is met when a negligible number of samples (e.g., zero or one) within the pause interval exceeds the maximum amplitude limit. In one example, the other example amplitude criterion defines the maximum amplitude limit as equal to the maximum amplitude minus a first parameter, where the first parameter is a threshold amplitude value. In another example, the other example amplitude criterion defines a second parameter as a threshold number of samples that cross the maximum amplitude limit. If the other example amplitude criterion is met, or both the example amplitude criterion and the other example amplitude criterion are met (the "yes" branch of 126), the pause-triggered episode may be a pseudopause.

[0076] based on FIG. 6 The example operation ends (No branch of 126), for example, because a false arrest triggering event detection criterion is not met, or the number or combination of false arrest triggering event detection criteria that are met is insufficient, the processing circuitry 80 can classify the suspected or potential arrest triggering event as a true arrest triggering event (128). Based on the arrest triggering event being classified as true, the processing circuitry 80 can use the arrest triggering event in additional operations such as calculating statistics, determining the patient's condition, or transmitting the true event data to other devices. If one or more of at least one amplitude criterion are met (Yes branch of 126), the processing circuitry 80 determines that the arrest triggering event is likely a false arrest, and FIG. 6 The example operation ends (130). Therefore, if at least one of the above three criteria for detecting pseudo-pause triggering episodes is met, the pause triggering episode may be a pseudo-pause. Based on the determination that the suspected pause triggering episode is a pseudo-pause triggering episode (130), the processing circuit system 80 may use the pseudo-pause triggering episode in other operations such as calculating statistics of the pseudo-epidemic and transmitting the pseudo-epidemic data to other devices, for example, to allow the user to consider modifying the IMD 10 to avoid further detection of pseudo-pause triggering episodes.

[0077] FIG. 7 This is a flowchart illustrating an example operation for classifying pause-triggered episodes based on whether or not the false pause-triggered episode detection criteria are met. According to... FIG. 7 In the example shown, the processing circuitry system 80 of the external device 12 classifies arrest-triggered episodes into high-priority, normal-priority, or low-priority arrests.

[0078] exist FIG. 7 In the example shown, the processing circuitry system 80 determines whether at least one spurious arrest triggering event detection criterion (150) is met. The processing circuitry system 80 performs this determination by applying at least one spurious arrest triggering event detection criterion to various datasets associated with cardiac EGM used for arrest triggering events. FIG. 6The example operation illustrates an example determination performed by the processing circuitry system 80, where first, second, and third pseudo-pause triggering event detection criteria are applied to determine whether a pause triggering event in the cardiac EGM is a possible pseudo-pause or a possible real pause. If none of the applicable pseudo-pause triggering event detection criteria are met (the "No" branch of 150), the processing circuitry system 80 determines that the pause triggering event is likely a real pause and classifies the real pause triggering event as high priority (152); otherwise, the processing circuitry system 80 determines that the pause triggering event is likely a pseudo-pause and further classifies the pause triggering event by applying one or more additional pseudo-pause triggering event detection criteria (the "Yes" branch of 150). When a pause triggering event is classified as a high-priority pause trigger, the processing circuitry system 80 may continue to insert the high-priority pause trigger into the review queue at a position prior to any normal or low-priority pause triggering event.

[0079] If any one of the four or more additional criteria for detecting pseudo-pause triggering episodes is not met, the processing circuit system 80 classifies the possible pseudo-pause triggering episodes as normal priority; conversely, if at least one of the four or more additional criteria is met, the possible pseudo-pause triggering episodes are classified as low priority. Therefore, the four or more additional criteria can be considered as classification criteria.

[0080] exist FIG. 7 In the example shown, for the first additional criterion, the processing circuitry 80 determines whether a second normal beat count criterion (154) is met. In some cases, the first normal beat count criterion may include a threshold number of normal beats, and the second normal beat count criterion may include even fewer normal beats. Similar to the first normal beat count criterion, the second normal beat count criterion may be met when the number of detected normal beats is less than or equal to the second threshold. Similar to the first normal beat count criterion, the second threshold may be a fixed number or a specific ratio or percentage of the normal beat count to the total beat count. When the first normal beat count criterion indicates a possible false arrest, the second normal beat count criterion indicates a more likely or very likely false arrest.

[0081] In response to a determination that the second normal beat count criterion is met ("Yes" branch of 154), processing circuitry 80 classifies the possible false pause-trigger episode as low priority (162). In response to a determination that the second normal beat count criterion is not met ("No" branch of 154), processing circuitry 80 continues to apply a second additional criterion having as conditions a last pre-pause beat that is noisy and a sufficient number of samples (e.g., two (2) samples) across an amplitude limit (e.g., a maximum amplitude limit) (156). In some examples, processing circuitry 80 accesses an amplitude threshold as a parameter and sets the amplitude limit to a difference between a maximum amplitude value and the parameter.

[0082] In response to a determination that the second additional criterion is not met ("No" branch of 156), processing circuitry 80 continues to apply a third additional criterion having as conditions a first normal beat count and a first maximum-minimum amplitude difference criterion (158). The first maximum-minimum amplitude difference criterion refers to a threshold difference between a comparison window maximum-minimum amplitude difference and a candidate window maximum-minimum amplitude difference. As described herein, the first maximum-minimum amplitude difference criterion is used as one of the false pause-trigger episode detection criteria for determining whether a pause-trigger episode is a false pause or a true pause. The candidate window refers to a pause time interval, and the comparison window refers to a second non-overlapping time interval in the same cardiac EGM containing normal beats. Comparing the amplitude values in these time intervals determines a relative flatness of the cardiac EGM data of the pause-trigger episode.

[0083] The satisfaction of these conditions by the possible false pause-trigger episode indicates to processing circuitry 80 that the third additional criterion is met ("Yes" branch of 158), thereby classifying the possible false pause-trigger episode as low priority (162). An example of the first maximum-minimum amplitude difference criterion includes a threshold difference of less than 2000 for a difference between a maximum amplitude and a minimum amplitude difference in two different time intervals; satisfying the first maximum-minimum amplitude difference criterion depends on whether the actual difference between the difference of the maximum-minimum amplitude difference is less than or equal to the threshold difference described above. The respective values of the maximum and minimum amplitude values can be extracted from samples in a first time interval containing the possible false pause and from samples in a second time interval containing a number of seconds equal to the first time interval that is non-overlapping. The samples in the second time interval define a pattern of normal cardiac electrical activity (e.g., a measure of amplitude variation such as flatness). Comparing the first and second time intervals according to the maximum-minimum amplitude value difference determines a relative flatness of the possible false pause-trigger episode.

[0084] Based on a determination that the third additional criterion is not met (the“NO” branch of 158), processing circuitry 80 applies a fourth additional criterion (160). One example of the fourth additional criterion includes a second maximum-minimum amplitude difference threshold value. The second maximum-minimum amplitude difference criterion can be a lower threshold value than the first maximum-minimum amplitude difference threshold, which even more strongly indicates a false pause due to the relative flatness of the amplitude values.

[0085] In response to a determination that the fourth additional criterion is met (the“YES” branch of 160), processing circuitry 80 determines that the possible false pause trigger episode will be classified as a low-priority and processing circuitry 80 FIG. 7 The example operation of ends (162). Based on the determination that the pause trigger episode is a low-priority false pause trigger episode (162), processing circuitry 50 can prioritize other episodes over the false pause trigger episode. In response to a determination that the fourth additional criterion is not met (the“NO” branch of 160), processing circuitry 80 determines that the possible false pause trigger episode is classified as a normal-priority false pause trigger episode. Based on the determination that the pause trigger episode is a normal-priority false pause trigger episode (the“NO” branch of 160), processing circuitry 50 can prioritize low-priority pause trigger episodes and pause trigger episodes below high-priority pause trigger episodes. FIG. 7 The example operation of ends (the“NO” branch of 160), for example, because none of the false pause trigger episode detection criteria are met, or the number or combination of false pause trigger episode detection criteria that are met is insufficient, processing circuitry 50 can classify the pause trigger episode as a normal-priority false pause trigger episode (164). Based on being classified as a normal-priority pause trigger episode, processing circuitry 50 can prioritize low-priority pause trigger episodes and pause trigger episodes below high-priority pause trigger episodes.

[0086] FIG. 6 and FIG. 7 The order and flow of operations shown in and are examples. In other examples according to the present disclosure, more or fewer criteria can be considered, the false pause trigger episode detection criteria and additional classification criteria can be considered in a different order, or a different number of false pause trigger episode detection criteria or combinations thereof can need to be met to determine that a pause trigger episode is false, and a different number of additional classification criteria or combinations thereof can be needed to determine whether a pause trigger episode should be prioritized as a low-priority or a normal-priority. Further, in some examples, processing circuitry can or can not perform the methods of and / or any of the techniques described herein, e.g., by external device 12 or computing device 100, as directed by a user. For example, a patient, clinician, or other user can turn on or off the functionality for remotely (e.g., using Wi-Fi or cellular service) or locally (e.g., using an application provided on a patient’s cell phone or using a medical device programmer) identifying asystole detections. FIG. 6 and / or FIG. 7 For example, a patient, clinician, or other user can turn on or off the functionality for remotely (e.g., using Wi-Fi or cellular service) or locally (e.g., using an application provided on a patient’s cell phone or using a medical device programmer) identifying asystole detections.

[0087] Additionally, although described in the context of examples in which the external device 12 and processing circuitry 80 of the external device 12 perform each of the portions of the example operations, FIG. 6 example operations described herein with respect to FIG. 7 may be performed by any processing circuitry of any one or more devices of a medical system, such as any combination of one or more of processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, processing circuitry 98 of server 94, or processing circuitry of computing device 100. In some examples, processing circuitry 50 of IMD 10 can determine whether the pause-triggered episode detection criteria are met and provide episode data of a pause-triggered episode to another device. In such examples, processing circuitry of the other device (e.g., external device 12, server 94, or computing device 100) can apply one or more pseudoparoxysmal pause detection criteria to the episode data.

[0088] FIGS. 8A-8B are illustrations of cardiac electrograms 200 and 220. Both cardiac electrograms 200, 220 depict possible pause-triggered episodes over respective time intervals. FIG. 8A Example cardiac electrogram (EGM) 200 is shown that exhibits a true pause episode, and FIG. 8B Example cardiac EGM 220 is shown that exhibits a pseudoparoxysmal pause.

[0089] As FIG. 8A shown, the samples in the three (3) second time intervals 201 record zero (0) beat detection values and flat amplitude values; in contrast, the three (3) second time intervals 221 exhibit zero (0) beat detection values, but have substantial amplitude changes (e.g., caused by noise) that indicate a pseudoparoxysmal pause-triggered episode. FIG. 8B shown, the samples in the three (3) second time intervals 201 record zero (0) beat detection values and flat amplitude values; in contrast, the three (3) second time intervals 221 exhibit zero (0) beat detection values, but have substantial amplitude changes (e.g., caused by noise) that indicate a pseudoparoxysmal pause-triggered episode.

[0090] As described herein, the relative flatness of amplitude values in a possible pause trigger onset distinguishes a true pause from a false pause. One or more amplitude criteria for defining the relative flatness in a first interval of a cardiac EGM can be used in one or more false pause trigger onset criteria. If a value measuring the change in amplitude in the first interval (e.g., the maximum-minimum amplitude difference) exceeds a threshold, these values can indicate a false pause. The threshold can be defined using amplitude values in a second time interval, or can be predetermined. If the amplitude change exceeds the threshold, the cardiac EGM is likely to be a false pause.

[0091] Another example false pause trigger onset criterion identifies a lack of a sufficient number of normal beats as an indication of a false pause. This is in part because a lack of normal beats cannot be determined to be a true pause. As FIG. 8A depicted, a sufficient number of normal beats are detected in the cardiac EGM 200, and as FIG. 8B depicted, the cardiac EGM 220 includes an insufficient normal beat count. The sufficiency can be defined as a normal beat count threshold (i.e., a first normal beat count criterion as described herein), and if the number of normal beats is less than or equal to this threshold, the pause can be a false pause. In some cases, if the number of normal beats is less than or equal to a second, lower threshold, the pause is likely to be a false pause and is classified as low priority. Comparing FIG. 8A and FIG. 8B The difference in beat morphology can be visualized such that the morphology analysis will identify FIG. 8B a lack of a sufficient number of normal beats in the cardiac EGM 220.

[0092] Yet another example false pause trigger onset criterion includes a noise status of the beat 202 preceding the last pause that indicates a false pause. In some examples, a noisy beat preceding the last pause indicates noise in the time interval containing the pause trigger onset. The samples and corresponding data set in the time interval are unreliable and cannot be used in confirming a true pause.

[0093] FIGS. 9A-9B are illustrations of cardiac EGMs and demonstrate the determination of the relative flatness of a potential pause trigger onset. FIG. 9A An example EGM 230 and time intervals 231 and 232 for determining the relative flatness of a true pause are shown. FIG. 9B An example EGM 240 and time intervals 241 and 242 of a false pause onset for determining the relative flatness of a false pause are shown.

[0094] FIG. 9A time intervals 231 and 232 of the EGM 230 and FIG. 9BIntervals 241 and 242 are used in one or more of the false pause-triggered episode onset detection criteria described herein for determining relative flatness as a function of amplitude values (i.e., at least one amplitude criterion). By applying at least one amplitude criterion to the amplitude values of example EGMs 230 and 240, the described medical systems and techniques (e.g., medical system 2) can distinguish between example EGM 230 (a true pause episode) and example EGM 240 (a false pause episode). One example amplitude criterion compares the amplitude variation measurements between the first interval 231 and the second interval 232 (or between the first interval 241 and the second interval 242), and the example amplitude criterion is satisfied when the comparison indicates a substantial amount of amplitude variation. An example of a substantial amount of amplitude variation occurs when the maximum-minimum amplitude difference of the first interval 231 is greater than the maximum-minimum amplitude difference of the second interval 232. Another example of a substantial amount of amplitude variation occurs when the maximum-minimum amplitude difference of the second interval 232 is greater and the difference between the maximum-minimum amplitude difference of the first interval 231 is less than a threshold difference.

[0095] To determine the relative flatness of a given pause-triggered episode, two time intervals within the episode are selected. Since the pause-triggered episode lasts 3 seconds, the following description utilizes a 2.5 second time; thus, the described medical systems and techniques are not limited to a particular amount of time, but can be set to any suitable amount of time that is less than the duration of the pause-triggered episode. If the pause-triggered episode lasts 5 seconds, the time intervals can be set to 4.5 or 5 seconds. The described medical systems and techniques can also depend on a pause-triggered episode duration threshold and / or an amount of EGM data stored relative to the pause-triggered episode detection. Generally, the candidate window is a time interval between a time point 0.5 seconds after the last pre-pause beat and a time point of the pause detection. In FIGS. 9A-9B In the example of FIG. 2, all of the last pre-pause beats are sensed at a 12 second time point (which is shown as a marker) and all of the pause detections are sensed at a 15 second time point, forming a candidate window between the 12.5 second time point and the 15 second time point. If example EGMs 230 and 240 stored additional pre-pause ECG data and / or if the pause-triggered episode duration threshold was set to a time length other than 3 seconds, the described medical systems and techniques can vary the time interval.

[0096] In one example, the first time interval (i.e., candidate window) consists of the EGM between the 12.5 second marker and the 15 second marker of the pause trigger onset, when the pause occurs between the 12 second marker and the 15 second marker. The first time interval tends to be relatively flat in a true pause onset, while a false pause onset tends to have artifact / noise signals and / or beats that are not detected by the device during this time. The start time of this window is the 12.5 second marker (rather than the 12 second marker) to avoid a wide QRS complex or T wave following the last beat before the 12 second marker. For the second time interval, a comparison window of 2.5 seconds duration is selected from the pause trigger onset.

[0097] The comparison window of the second time interval must satisfy multiple conditions, such as the following conditions: 1) the comparison window should not overlap with the 12.5-15 second candidate window; 2) the 2.5 second comparison window should not have any noise or PVC beats; and 3) the comparison window must contain at least two normal beat markers.

[0098] The second time interval is determined by searching from the first 500 ms of the onset and moving the window until an acceptable 2.5 second window is obtained that satisfies all three of the above conditions. If a 2.5 second window does not satisfy all three conditions, the window is moved to the next beat marker and the 3 seconds after the marker are analyzed for the three conditions. If the conditions are satisfied, the comparison window is selected from the 0.5 second marker before the beat marker until the 3 second marker (2.5 seconds total).

[0099] After an acceptable comparison window is found to be used as the second time interval, the following steps are performed to determine whether the first time interval indicates a true pause or a false pause: 1) subtract the mean from the 2.5 second ECG in the comparison window; 2) find the minimum and maximum amplitude values in the comparison window; 3) calculate the comparison window difference (e.g., maximum amplitude minus minimum amplitude); 4) repeat steps 1-3 for the candidate window of the first time interval and calculate the candidate window difference (e.g., maximum amplitude minus minimum amplitude); and 5) calculate the final difference between the comparison window and the candidate window (e.g., comparison window difference minus candidate window difference).

[0100] For a true pause onset, the comparison window difference is expected to be high due to the true pause, while the candidate window difference is expected to be relatively low. Thus, we can expect to see a large and positive final difference (e.g., any value between 0 and 20,000). However, for a false pause onset, the candidate window and comparison window differences are expected to be closer, and more of the final difference is expected to be negative or closer to zero.

[0101] A second false pause onset detection criterion corresponding to amplitude values includes a maximum amplitude goal for normal beats in the first time interval. With this maximum amplitude goal, medical system 2 determines how many samples in the candidate window have amplitude values that exceed the maximum amplitude limit for the first time interval. If the samples that cross this limit exceed the maximum amplitude goal, the probability of a true and false onset is reduced.

[0102] Computing the maximum amplitude limit can include performing the following steps: 1) for each normal beat marker in the 45 second cardiac EGM, computing the maximum amplitude value in one or more (e.g., 20) samples before and after the marker; 2) computing the median of the maximum amplitudes for all normal beats; 3) computing the number of samples in a 2.5 second candidate window (e.g., from the 12.5 second marker in the onset to the 15 second marker) where the amplitude crosses the (median - first parameter) value; and 5) comparing the number of samples to a second parameter. If the number of samples that cross the (median - first parameter) value is greater than the second parameter, medical system 2 classifies the onset as a false pause.

[0103] The first and second parameters can be optimized to achieve a desired balance between sensitivity and specificity for the relative flatness of the cardiac EGM. Medical system 2 can calibrate the first and second parameters to achieve different pairs of sensitivity and specificity values for the classification and prioritization of cardiac EGM data for pause-triggered onsets. An example of the first parameter can be a value of 500, and an example of the second parameter can be zero (0), one (1), or two (2).

[0104] FIGS. 10A-10C FIG. 40 is a conceptual diagram of another example medical system 410 implanted within a patient 408. FIG. 10A FIG. 41 is a front view of medical system 410 implanted within patient 408. FIG. 10B FIG. 42 is a side view of medical system 410 implanted within patient 408. FIG. 10C FIG. 43 is a lateral view of medical system 410 implanted within patient 408.

[0105] In some examples, medical system 410 is an extravascular implantable cardioverter-defibrillator (EV-ICD) system implanted within patient 408. Medical system 410 includes IMD 412, which in the illustrated example is implanted subcutaneously or submuscularly on the left midaxillary region of patient 408, such that IMD 412 can be positioned over the left side of the thorax of patient 408. In some other examples, IMD 412 can be implanted at other subcutaneous locations on patient 408, such as at a pectoral location or an abdominal location. IMD 412 includes a housing 420 that can form a hermetic seal protecting the components of IMD 412. In some examples, housing 420 of IMD 412 can be formed of a conductive material, such as titanium, or a combination of conductive and non-conductive materials that can serve as housing electrodes. IMD 412 can also include a connector assembly (also referred to as a connector block or header) that includes electrical feedthroughs through which electrical connections are made between the leads 422 and electronic components contained within the housing.

[0106] IMD 412 can provide cardiac EGM sensing, asystole detection, and other functions described herein with respect to IMD 10, and housing 420 can house circuitry 50-62 and antenna 26 FIG. 2 and 3 ) providing such functions. Housing 420 can also house therapy delivery circuitry configured to generate therapeutic electrical signals for delivery to patient 408, such as cardiac pacing and anti-tachyarrhythmia shocks. System 410 can include external device 12 that can work with IMD 412, as described herein with respect to IMD 10 and system 2.

[0107] In the illustrated example, IMD 412 is connected with at least one implantable cardiac lead 422. Lead 422 includes an elongated lead body having a proximal end including a connector (not shown) configured to connect with IMD 412 and a distal portion including electrodes 432A, 432B, 434A, and 434B. Lead 422 extends subcutaneously from IMD 412 over the thorax toward the center of the torso of patient 408. At a location near the center of the torso, lead 422 curves or turns and extends upward within the thoracic cavity below / under the sternum 424. Thus, lead 422 can be implanted at least partially in the substernal space, such as at a target site between the thorax or sternum 424 and heart 418. In one such configuration, a proximal portion of lead 422 can be configured to extend subcutaneously from IMD 12 toward the sternum 24, and a distal portion of lead 422 can be configured to extend upward in the prevertral mediastinum 426 below or under the sternum 424 FIG. 10C

[0108] ​For example, the lead 422 can extend within the anterior mediastinum 426 within the thoracic cavity superiorly below / beneath the sternum 424. The anterior mediastinum 426 can be considered as being bounded posteriorly by the pericardium 416, laterally by the pleura 428, and anteriorly by the sternum 424. In some examples, the anterior wall of the anterior mediastinum 426 can also be formed by the transversus thoracis muscle and one or more costal cartilages. The anterior mediastinum 426 contains some amount of loose connective tissue (e.g., honeycomb tissue), some lymphatic vessels, lymph glands, sub-sternal muscle tissue (e.g., the transversus thoracis muscle), and small blood vessels or vascular branches. In one example, the distal portion of the lead 422 can be implanted substantially within the loose connective tissue and / or the sub-sternal muscle tissue of the anterior mediastinum 426. In such examples, the distal portion of the lead 422 can be physically isolated from the pericardium 416 of the heart 418. Leads implanted substantially within the anterior mediastinum are examples of sub-sternal leads or more generally extravascular leads.

[0109] The distal portion of the lead 422 is described herein as being implanted substantially within the anterior mediastinum 426. Thus, some of the distal portion of the lead 422 can extend outside of the anterior mediastinum 426 (e.g., a proximal end of the distal portion), but a majority of the distal portion can be positioned within the anterior mediastinum 426. In other embodiments, the distal portion of the lead 422 can be implanted intrathoracically in other non-vascular, extra-pericardial locations, including around the periphery of and adjacent to but not attached to the pericardium 416 or other portions of the heart 418 and not in the gap, tissue, or other anatomical feature above the sternum 424 or ribcage. The lead 422 can be implanted in any location within the “sub-sternal space” defined by the lower surface between the sternum and / or ribcage and the body cavity, but not including the pericardium 416 or other portions of the heart 418. As known to those skilled in the art, the sub-sternal space can alternatively be referred to by the terms “retrosternal space” or “mediastinum” or “infrasternal” and includes the anterior mediastinum 426. The sub-sternal space can also include the anatomical region described in Baudoin, Y. P., et al., “The superior epigastric artery does not pass through Larrey’s space (trigonum sternocostale),” Surg. Radiol. Anat. 25.3-4 (2003): 259-62, as Larrey’s space. In other words, the distal portion of the lead 422 can be implanted in a region around the outer surface of the heart 418, but not attached to the heart 418. For example, the distal portion of the lead 422 can be physically isolated from the pericardium 416.

[0110] Lead 422 can include an insulated lead body having a proximal portion including a connector 430 configured to connect with IMD 412 and a distal portion including one or more electrodes. As FIG. 10A shown, the one or more electrodes of lead 422 can include electrodes 432A, 432B, 434A, and 434B, although in other examples, lead 422 can include more or fewer electrodes. Lead 422 also includes one or more conductors that form electrically conductive paths within the lead body and interconnect the electrical connector and respective ones of the electrodes.

[0111] Electrodes 432A, 432B can be defibrillation electrodes (individually or collectively, "defibrillation electrodes 432"). Although electrodes 432 can be referred to herein as "defibrillation electrodes 432," electrodes 432 can be configured to deliver other types of anti-tachyarrhythmia shocks, such as cardioversion shocks. Although defibrillation electrodes 432 are depicted in FIGS. 10A-10C as coil electrodes for the sake of clarity, it should be understood that defibrillation electrodes 432 can be other configurations in other examples. Defibrillation electrodes 432 can be positioned on a distal portion of lead 422, where the distal portion of lead 422 is the portion of lead 422 that is configured to be implanted extravascularly under sternum 424.

[0112] Lead 422 can be implanted under sternum 424 or at a target site along the sternum such that a therapy vector is substantially across a ventricle of heart 418. In some examples, the therapy vector (e.g., a shock vector for delivering anti-tachyarrhythmia shocks) can be between defibrillation electrodes 432 and a housing electrode formed by or on IMD 412. In one example, the therapy vector can be thought of as a line extending from a point on defibrillation electrodes 432 (e.g., a center of one of defibrillation electrodes 432) to a point on a housing electrode of IMD 412. As such, this can be advantageous for increasing an amount of area that defibrillation electrodes 432 (and, where the distal portion of lead 422) extend across heart 418. Accordingly, lead 422 can be configured to define a curved distal portion as depicted in FIG. 10A In some examples, the curved distal portion of lead 22 can help improve the efficacy and / or efficiency of pacing, sensing, and / or defibrillation of heart 418 by IMD 412.

[0113] Electrodes 434A, 434B can be pacing / sensing electrodes (individually or collectively, "pacing / sensing electrodes 434") positioned on a distal portion of lead 422. Electrodes 434 are referred to herein as pacing / sensing electrodes because they are typically configured for the delivery of pacing pulses and / or the sensing of cardiac electrical signals. In some cases, electrodes 434 can provide pacing functions only, sensing functions only, or both pacing and sensing functions. In FIG. 10A and FIG. 10B In the illustrated example, pacing / sensing electrodes 434 are spaced apart from each other by defibrillation electrode 432B. However, in other examples, pacing / sensing electrodes 434 can both be distal to defibrillation electrode 432B or both be proximal to defibrillation electrode 432B. In examples in which lead 422 includes more or fewer electrodes 432, 434, such electrodes can be positioned at other locations on lead 422.

[0114] In examples in which lead 422 includes more or fewer electrodes 432, 434, such electrodes can be positioned at other locations on lead 422. FIG. 10A In the illustrated example, the distal portion of lead 422 is a serpentine shape that includes two "C" curves that together can resemble the Greek letter "epsilon." Defibrillation electrodes 432 are each carried by one of the two respective C-shaped portions of the distal portion of the lead body. The two C curves extend or bend away from the central axis of the lead body in the same direction. In some examples, pacing / sensing electrodes 434 can be approximately aligned with the central axis of the straight proximal portion of lead 422. In such examples, the midpoints of defibrillation electrodes 432 are laterally offset from pacing / sensing electrodes 434. Other examples of extravascular leads including one or more defibrillation electrodes carried by a curved, serpentine, undulating, or zigzag-shaped distal portion of a lead and one or more pacing / sensing electrodes 434 can also be used using the techniques described herein. In some examples, the distal portion of lead 422 can be straight (e.g., straight or close to straight).

[0115] Deploying lead 422 such that electrodes 432, 434 are at the peaks and valleys of the depicted serpentine shape can provide access to a preferred sensing or therapy vector. Adjusting the orientation of a serpentine-shaped lead such that pacing / sensing electrodes 434 are closer to heart 418 can provide better electrical sensing of cardiac signals and / or lower pacing capture thresholds than if pacing / sensing electrodes 434 were oriented farther from heart 418. The serpentine or other shape of the distal portion of lead 422 can increase fixation to patient 408 because the shape provides resistance to adjacent tissue when axial forces are applied. Another advantage of a shaped distal portion is that electrodes 432, 434 can achieve a greater surface area over a shorter length of heart 418 relative to a lead having a straighter distal portion.

[0116] In some examples, the elongated lead body of lead 422 can include one or more elongated electrical conductors (not shown) that extend within the lead body from the connector at the proximal lead end to electrodes 432, 434 positioned along a distal portion of lead 422. The one or more elongated electrical conductors included within the lead body of lead 422 can be engaged with respective ones of electrodes 432, 434. The conductors can be electrically coupled through the connection assembly to circuitry of IMD 412, such as therapy delivery circuitry and sensing circuitry 52. The electrical conductors transmit therapy from the therapy delivery circuitry to one or more of electrodes 432, 434, and transmit sensed cardiac EGMs from one or more of electrodes 432, 434 to sensing circuitry 52 within IMD 412.

[0117] Generally, IMD 412 can sense cardiac EGMs as by one or more sensing vectors that include a combination of pacing / sensing electrodes 434 and / or housing electrodes of IMD 412. In some examples, IMD 412 can sense EGMs using sensing vectors that include one or both of defibrillation electrodes 432 and / or one of defibrillation electrodes 432 and one of pacing / sensing electrodes 434 or a housing electrode of IMD 412. Processing circuitry of medical system 410, including IMD 412 and / or external device 12, can perform any of the techniques described herein for determining whether cardiac arrest and pseudo cardiac arrest detection criteria are met, for example, based on cardiac EGMs sensed by extravascular electrodes 432, 434. Cardiac EGMs sensed by extravascular electrodes can include noise, for example, due to contact with tissue and / or changes in orientation relative to the heart, in a similar manner as described herein with respect to subcutaneous electrodes. Generally, when electrodes are not directly fixed to heart muscle, motion, such as respiratory motion, causes changes in depolarization amplitude and other noise that can cause pseudo cardiac arrest detections. The techniques described herein can be implemented with cardiac EGMs sensed by subcutaneous electrodes, skin electrodes, substernal electrodes, extravascular electrodes, intramuscular electrodes, or any electrodes positioned in (or in contact with) any tissue of a patient.

[0118] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the techniques can be implemented within one or more processors, 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, image sources, or other devices. The terms "processor" and "processing circuitry" can generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent electrical circuitry, alone or in combination with other digital or analog circuitry.

[0119] For aspects implemented in software, at least some of the functionality attributed to the systems and devices described in this disclosure can be embodied as instructions on a computer-readable storage medium, such as RAM, DRAM, SRAM, magnetic disks, optical disks, flash memory, or forms of EPROM or EEPROM. The instructions can be executed by one or more processing units to support one or more aspects of the functionality described in this disclosure.

[0120] Also, in some aspects, the functions described herein can be implemented in software and / or hardware modules. The different features described herein can be implemented as software modules or code (e.g., as one or more instructions) stored on a computer-readable medium, which can be executed by a processing unit. As used herein, the term "computer- readable medium" encompasses only a computer-readable medium that actually renders computer-executable instructions. Thus, a computer-readable medium that is a carrier wave is not encompassed by the term computer- readable medium as used herein. The modules can be software modules (e.g., code, instructions, or instructions sets) that are stored and executed by a processing unit. As used herein, a software module or code can also be simply referred to as "software." Also, the modules can be hardware modules or components that carry out the techniques described herein, or any combination thereof. Furthermore, the modules can be implemented as hardware modules associated with a medical device, such as an IMD, an external programmer, a combination of an IMD and an external programmer, an integrated circuit (IC), or a set of ICs, and / or discrete electronic circuitry that is resident within an IMD and / or an external programmer.

Claims

1. A medical system comprising a processing circuitry and a storage medium, wherein the processing circuitry is configured to: Receive cardiac electrograms of arrest-triggered episodes, the cardiac electrograms being sensed by a medical device via multiple electrodes; Based on the electrocardiogram, it is determined whether one or more criteria for detecting false arrest are met, wherein the one or more criteria for detecting false arrest include: At least one criterion for the relative flatness of the amplitude values ​​of the cardiac electrogram during the time interval between the last pre-pause beat and the time of the pause detection, wherein the at least one criterion includes a criterion that is satisfied based on determining that the maximum-minimum amplitude difference in the time interval is greater than or equal to the maximum-minimum amplitude difference in another time interval; Based on whether the false arrest detection criteria are met, the arrest-triggered episodes are classified into one of several categories; and The classification of the arrest-triggered attacks is output to the user's display.

2. The medical system of claim 1, wherein the plurality of classifications includes true cardiac arrest and pseudocardiac arrest.

3. The medical system of claim 1, wherein each of the plurality of categories includes a corresponding priority marker.

4. The medical system of claim 3, wherein the processing circuitry is further configured to insert the cardiac electrogram into one of a plurality of review queues based on the priority flag.

5. The medical system of claim 1, wherein the processing circuitry is configured to: Based on the electrocardiogram, determine whether one or more of the false arrest detection criteria are met; and Based on the determination that none of the aforementioned false arrest detection criteria are met, arrest-triggered episodes are classified as high-priority possible true arrest episodes.

6. The system of claim 5, wherein the processing circuitry is further configured to: Based on the electrocardiogram, determine whether at least one additional criterion for detecting pseudopause is met; and Based on the determination that at least one additional spurious arrest detection criterion is met, the arrest-triggered episode is classified as a low-priority possible spurious arrest episode.

7. The system of claim 6, wherein the processing circuitry is further configured to classify the arrest-triggered episode as a possible pseudo-arrest episode with normal priority based on the determination that none of the at least one of the additional pseudo-arrest detection criteria is not met.

8. The medical system of claim 1, wherein the one or more false arrest detection criteria include a criterion comprising a threshold number of normal beats on the electrocardiogram.

9. The medical system of claim 1, wherein the one or more false arrest detection criteria include criteria for determining that a normal beat in the electrocardiogram is less than a normal beat threshold.

10. The medical system of claim 1, wherein the one or more false arrest detection criteria include criteria based on the noise state of the last pre-arrest beat on the electrocardiogram.

11. The medical system of claim 1, wherein the one or more false arrest detection criteria include criteria based on the identification of a noisy pre-arrest beat in the electrocardiogram.

12. The medical system of claim 1, wherein the at least one criterion includes a criterion based on the satisfaction of the amplitude value in the uncertain time interval being relatively flat compared to another time interval.

13. The medical system according to claim 1, characterized in that, The maximum-minimum amplitude difference in the time interval refers to the difference between the maximum and minimum amplitudes within the time interval containing the pause, and the maximum-minimum amplitude difference in the other time interval refers to the difference between the maximum and minimum amplitudes within another time interval containing normal pulsation in the same cardiac electrocardiogram.

14. A non-transitory computer-readable medium comprising program instructions that, when executed by a processing circuitry of a medical system, cause the processing circuitry to: Receive cardiac electrograms of arrest-triggered episodes, the cardiac electrograms being sensed by a medical device via multiple electrodes; Based on the electrocardiogram, it is determined whether one or more criteria for detecting false arrest are met, wherein the one or more criteria for detecting false arrest include: At least one criterion for the relative flatness of the amplitude values ​​of the cardiac electrogram during the time interval between the last pre-pause beat and the time of the pause detection, wherein the at least one criterion includes a criterion that is satisfied based on determining that the maximum-minimum amplitude difference in the time interval is greater than or equal to the maximum-minimum amplitude difference in another time interval; Based on whether the false arrest detection criteria are met, the arrest-triggered episodes are classified into one of several categories; and The classification of the arrest-triggered attacks is output to the user's display.

15. A medical system comprising a processing circuitry system and a non-transitory computer-readable medium according to claim 14.

Images