阅读说明：本技术 假心搏停止检测的标识 (Identification of false asystole detection ) 是由 郑雅健 J·D·瑞兰德 于 2020-03-25 设计创作，主要内容包括：本公开涉及用于标识心脏电描记图中的假心搏停止检测的技术,所述技术包含确定是否满足多个假心搏停止检测标准中的至少一个假心搏停止检测标准。在一些实例中,所述多个假心搏停止检测标准包含：第一假心搏停止检测标准,所述第一假心搏停止检测标准包含用于检测所述心脏电描记图中的心脏去极化的降低的振幅阈值；以及第二假心搏停止检测标准,所述第二假心搏停止检测标准用于检测所述心脏电描记图中的衰减噪声。(The present disclosure relates to techniques for identifying false asystole detection in cardiac electrograms, the techniques including determining whether at least one false asystole detection criterion of a plurality of false asystole detection criteria is satisfied. In some examples, the plurality of false asystole detection criteria includes: a first false asystole detection criterion that includes a reduced amplitude threshold for detecting cardiac depolarization in the cardiac electrogram; and a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.)

1. A medical system, comprising:

a plurality of electrodes configured to sense a cardiac electrogram of a patient; and

processing circuitry configured to:

determining that cardiac arrest detection criteria are met based on the cardiac electrogram;

determining whether a plurality of false asystole detection criteria are met based on the cardiac electrogram signal based on the determination that the asystole detection is met; and is

Rejecting an indication of a cardiac arrest episode for the patient based on determining that at least one of the plurality of false cardiac arrest detection criteria is met,

wherein the plurality of false asystole detection criteria comprises:

a first false asystole detection criterion that includes a reduced amplitude threshold for detecting cardiac depolarization in the cardiac electrogram; and

a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

2. The medical system of claim 1, wherein the processing circuitry is configured to determine that the asystole detection criteria are met based on no cardiac depolarization being identified in the cardiac electrogram during a time interval.

3. The medical system of claim 2, wherein to determine that the first false cardiac arrest detection criteria are met, the processing circuitry is configured to:

comparing the reduced amplitude threshold to the cardiac electrogram over the time interval; and is

Determining, based on the comparison, that a threshold number of cardiac depolarizations were identified in the cardiac electrogram during the time interval.

4. The medical system of claim 2 or 3, wherein the processing circuitry is configured to:

identifying one or more cardiac depolarizations occurring in the cardiac electrogram prior to the time interval;

determining an amplitude of each of the one or more identified cardiac depolarizations; and is

Determining the reduced amplitude threshold based on the determined amplitudes of the one or more identified cardiac depolarizations.

5. The medical system of claim 4, wherein the one or more identified heart depolarizations comprises a plurality of identified heart depolarizations, and the processing circuitry is configured to:

determining a representative amplitude based on the amplitude of each of the plurality of identified cardiac depolarizations; and is

Determining the reduced amplitude threshold as a predetermined fraction of the representative amplitude.

6. The medical system of claim 2 or 3, wherein to determine that the second false cardiac arrest detection criteria are met, the processing circuitry is configured to:

calculating an area under the curve value of the cardiac electrogram during at least a portion of the time period; and is

Determining that the area under the curve value satisfies an area under the curve threshold.

7. The medical system of claim 2 or 3, wherein to determine that the second false cardiac arrest detection criteria are met, the processing circuitry is configured to:

determining a differential signal of the cardiac electrogram during at least a portion of the time period;

for each of a plurality of samples of the differential signal, determining whether a sign of the sample is positive or negative; and is

Determining that an amount of samples having one of the symbols satisfies a common symbol threshold.

8. The medical system of claim 2 or 3, wherein the plurality of false asystole detection criteria further comprises a third false asystole detection criteria, wherein to determine that the third false asystole detection criteria is met, the processing circuitry is configured to:

identifying a plurality of cardiac depolarizations that occurred in the cardiac electrogram prior to the time interval;

determining an amplitude of each of the plurality of identified cardiac depolarizations;

determining a variability of the amplitude; and is

Determining that the variability satisfies a variability threshold.

9. The medical system of claim 8, wherein to determine the variability of the amplitude, the processing circuitry is configured to:

determining a maximum amplitude of the plurality of amplitudes;

determining a representative amplitude of the plurality of amplitudes; and is

A comparative measure of the maximum amplitude and the representative amplitude is determined.

10. The medical system of claim 2 or 3, wherein the plurality of false asystole detection criteria further comprises a third false asystole detection criteria, wherein to determine that the third false asystole detection criteria is met, the processing circuitry is configured to:

identifying a plurality of cardiac depolarizations that occurred in the cardiac electrogram prior to the time interval;

determining one or more intervals between adjacent ones of the plurality of cardiac depolarizations;

based on the determined interval, identifying one or more expected cardiac depolarization windows and one or more expected inter-depolarization windows within the time interval;

determining a first energy of the one or more cardiac depolarization windows and a second energy of the one or more inter-depolarization windows;

determining a comparative measure of the first energy and the second energy; and is

Determining that the comparison metric satisfies a threshold.

11. The medical system of claim 1, wherein the processing circuitry is configured to:

determining a count of instances that meet the asystole detection criteria over a period of time; and is

Determining whether the count satisfies at least one asystole count criterion,

wherein the processing circuitry is configured to determine whether the plurality of false asystole detection criteria are met based on determining that the count meets at least one asystole count criterion.

12. The medical system of any of the preceding claims, wherein the plurality of electrodes are configured for subcutaneous implantation and the cardiac electrogram comprises a subcutaneous cardiac electrogram.

13. The medical system of any of the preceding claims, wherein the plurality of electrodes are configured for extravascular implantation and the cardiac electrogram comprises an extravascular cardiac electrogram.

Technical Field

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

Background

Some types of medical devices may monitor cardiac Electrograms (EGMs) of a patient to monitor electrical activity of the patient's heart. Cardiac EGMs are electrical signals sensed through electrodes. In some examples, the medical device monitors the cardiac EGM to detect one or more types of arrhythmias, such as bradycardia, tachycardia, fibrillation, or asystole (e.g., caused by sinus pause or AV block).

Disclosure of Invention

In addition to signals representing the electrical activity of the heart, cardiac EGMs may contain noise. Additionally, the amplitude of the signal representative of the electrical activity of the heart within the cardiac EGM may vary over time, for example, due to movement of the electrodes relative to the cardiac tissue. Noise and signal amplitude variations may confound the use of cardiac EGMs to detect arrhythmias, such as asystole.

In general, the present disclosure relates to techniques for identifying false asystole detection in cardiac electrograms. The techniques include analyzing a cardiac EGM to determine whether at least one of a plurality of false asystole detection criteria is satisfied. In some examples, processing circuitry of the medical device system performs this analysis in response to the asystole detection criteria being met, and may determine whether to provide or deny an indication (e.g., to a clinician or other user) that the patient experienced asystole based on the analysis. In this way, the techniques of the present disclosure may advantageously enable improved accuracy of identification of a true cardiac arrest, and thus a better assessment of the condition of the patient.

In one example, a medical system includes a plurality of electrodes configured to sense cardiac electrograms of a patient; and processing circuitry. The processing circuitry is configured to determine that a cardiac arrest detection criterion is met based on the cardiac electrogram, and determine whether a plurality of false cardiac arrest detection criteria are met based on the cardiac electrogram signal based on the determination that the cardiac arrest detection is met. The processing circuitry is further configured to reject the indication of the onset of cardiac arrest for the patient based on determining that at least one of the plurality of false cardiac arrest detection criteria is met. The plurality of false asystole detection criteria comprises a first false asystole detection criteria that includes a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram; and a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

In another example, a method comprises: a cardiac electrogram of a patient is sensed by a plurality of electrodes of a medical system, and a determination is made by processing circuitry of the medical system based on the cardiac electrogram that cardiac arrest detection criteria are met. The method further comprises: determining, by the processing circuitry, based on the cardiac electrogram signal, that at least one false asystole detection criterion of a plurality of false asystole detection criteria is satisfied based on the determining that the asystole detection is satisfied; and withholding, by the processing circuitry, an indication of a cardiac arrest episode for the patient based on determining that at least one of the plurality of false cardiac arrest detection criteria is satisfied. The plurality of false asystole detection criteria comprises a first false asystole detection criteria that includes a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram; and a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

In another example, a non-transitory computer-readable storage medium includes program instructions that, when executed by processing circuitry of a medical system, cause the processing circuitry to determine that asystole detection criteria are met based on cardiac electrograms sensed by a plurality of electrodes of the medical system. Based on the determination that the asystole detection is satisfied, the program instructions cause the processing circuitry to determine whether a plurality of false asystole detection criteria are satisfied based on the cardiac electrogram signal, and reject the indication of the onset of asystole in the patient based on the determination that at least one of the plurality of false asystole detection criteria is satisfied. The plurality of false asystole detection criteria comprises a first false asystole detection criteria that includes a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram; and a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

This summary is intended to provide an overview of the subject matter described in this disclosure. This summary is not intended to provide an exclusive or exhaustive explanation of the systems, apparatuses, and methods described in detail within the following figures and description. Further details of one or more examples of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

Drawings

FIG. 1 illustrates an environment of an example medical system in connection with a patient.

Fig. 2 is a functional block diagram illustrating an example configuration of an Implantable Medical Device (IMD) of the medical system of fig. 1.

Fig. 3 is a conceptual side view illustrating an example configuration of the IMD of fig. 1 and 2.

Fig. 4 is a functional block diagram illustrating an example configuration of the external device of fig. 1.

Fig. 5 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 may be coupled with the IMD and external devices of fig. 1-4.

Fig. 6 is a flowchart illustrating example operations for determining whether an identification of a asystole episode is false based on whether a plurality of false asystole detection criteria are met.

Fig. 7 is a diagram illustrating cardiac EGMs associated with identified asystole episodes and an example technique for determining whether example false asystole detection criteria are met based on the cardiac EGMs.

Fig. 8 is a flowchart illustrating example operations for determining whether an example false cardiac arrest criterion is met that includes a reduced amplitude threshold for depolarization detection.

Fig. 9 is a diagram illustrating a cardiac EGM including attenuated noise.

Fig. 10 is a diagram illustrating a cardiac EGM containing attenuated noise and an example technique for determining whether an example false asystole detection criterion is met based on the cardiac EGM.

Fig. 11 is a flowchart illustrating example operations for determining whether example false cardiac arrest criteria for detecting attenuating noise are met.

Fig. 12 is a diagram illustrating a differential signal of a cardiac EGM containing attenuated noise and an example technique for determining whether an example false asystole detection criterion is met based on the cardiac EGM.

Fig. 13 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria for detecting attenuating noise are met.

Fig. 14 is a diagram illustrating cardiac EGMs associated with identified asystole episodes and an example technique for determining whether another example false asystole detection criterion is satisfied based on the cardiac EGMs.

FIG. 15 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria are met.

Fig. 16 is a diagram illustrating cardiac EGMs associated with identified asystole episodes and an example technique for determining whether another example false asystole detection criterion is satisfied based on the cardiac EGMs.

FIG. 17 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria are met.

Fig. 18A is a conceptual diagram illustrating a front view of a patient with another example medical system.

Fig. 18B is a conceptual diagram illustrating a side view of a patient with the example medical system of fig. 18A.

Fig. 18C is a conceptual diagram illustrating a lateral view of a patient with the example medical system of fig. 18A.

Like reference numerals refer to like elements throughout the specification and drawings.

Detailed Description

Various types of medical devices sense cardiac EGMs. Some medical devices that sense cardiac EGMs are non-invasive, for example, using multiple 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 to monitor cardiac EGMs in these non-invasive procedures may be attached to a patient using an adhesive, a strap, a belt, or a vest, and electrically coupled to a monitoring device, such as an electrocardiograph, holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with electrical activity of the patient's heart or other cardiac tissue, and provide these sensed electrical signals to the electronics for further processing and/or display of the electrical signals. Non-invasive devices and methods may be utilized on a temporary basis, for example, to monitor patients during a clinical visit, such as during a doctor appointment, or for example, over a predetermined time period, such as a day (twenty-four hours), or a period of days.

External devices that may be used to non-invasively sense and monitor cardiac EGMs include wearable devices having electrodes configured to contact the skin of a patient, such as a patch, watch, or necklace. One example of a wearable physiological monitor configured to sense cardiac EGMs is SEEQ, commercially available from Medtronic pic plc, dublin, ireland^TMA mobile cardiac telemetry system. Such external devices may facilitate relatively long-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as Carelink of mayonney corporation^TMA network.

An Implantable Medical Device (IMD) may also sense and monitor cardiac EGMs. Electrodes used by IMDs to sense cardiac EGMs are typically integrated with the housing of the IMD and/or coupled to the IMD by one or more elongated leads. Example IMDs that monitor cardiac EGMs include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, and pacemakers having housings configured for implantation within the heart, which may be leadless. An example of a pacemaker configured for intracardiac implantation is Micra^TMTranscatheter pacing systems are available from mayonney. Some IMDs that do not provide therapy, such as implantable patient monitors, sense cardiac EGMs. An example of such an IMD is a subcutaneously insertable regenerative LINQ^TMA plug-in heart monitor, available from mayonney. Such IMDs may facilitate relatively long-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as Carelink by mayonney corporation^TMA network.

Regardless of which type or types of devices are used, a noise signal, which may be referred to as an artifact, may appear in the cardiac EGM. The duration of the noise signal may extend over a portion of the normal time frame of the cardiac cycle of the heart, or may extend over a span of time over which multiple cardiac cycles may be expected to have occurred. Such noise signals may be more prevalent when using cutaneous, subcutaneous, or extravascular electrodes to sense cardiac EGMs, for example, due to relative motion of the electrodes and tissue resulting in temporary changes in contact between at least one of the electrodes and the tissue in which the electrode is positioned. In some instances, the noise signal manifests as a baseline shift in the cardiac EGM and may contain portions that decay back toward a steady-state baseline.

The presence of a noise signal in a sensed cardiac EGM may cause circuitry used to detect depolarization, such as an R-wave, to erroneously detect the noise signal as depolarization. The noise signal may also cause the circuitry to then fail to sense multiple subsequent depolarizations because the amplitude of the noise signal may be much larger than the subsequent depolarizations, and in some cases because the high amplitude noise may cause the adjustable sensing threshold used by the circuitry to be adjusted to a level greater than the true depolarization amplitude. Additionally, the amplitude of the sensed cardiac signal, e.g., depolarization, within the cardiac EGM may change over time, e.g., due to respiration. Such cardiac signal amplitude variations may also be more prevalent in cardiac EGMs sensed using cutaneous, subcutaneous, or extravascular electrodes. Changes in cardiac signal amplitude may also cause depolarizations to temporarily fall below the sensing threshold and thus not be detected.

These types of incorrect sensing of depolarization may result in incorrect analysis of the actual cardiac activity occurring with respect to the monitored patient. For example, these types of incorrect sensing of depolarization may potentially trigger a false positive indication of a cardiac event, such as asystole, that does not actually occur in the patient. Such false positive indications may result in a false assessment 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 cardiac EGMs generally does not help solve these problems because these types of noise signals and amplitude variations may occur at frequencies near or below the frequency of the cardiac signal.

Medical systems according to the present disclosure implement techniques for identifying false asystole detection in cardiac EGMs by, for example, detecting the presence of noise signals and cardiac signal amplitude variations. In some examples, processing circuitry of the system analyzes cardiac EGMs associated with the identified asystole episode to determine whether one or more of a plurality of false asystole detection criteria are satisfied. Each of the false asystole detection criteria may be configured to detect one or more indicators of noise and/or amplitude variations in the cardiac EGM.

In some examples, processing circuitry of the medical system performs this analysis in response to the asystole detection criteria being met, and may determine whether to provide or deny an indication (e.g., to a clinician or other user) that the patient experienced asystole based on the analysis. The processing circuitry may perform the techniques of this disclosure in substantially real time in response to detection of cardiac arrest, or during later review of cardiac EGM data identified as the onset of cardiac arrest. In either case, the processing circuitry may include processing circuitry of the medical device that detected the asystole episode 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 may advantageously enable improved accuracy of identification of a true cardiac arrest, and thus a better assessment of the condition of the patient.

Fig. 1 illustrates an environment of an example medical system 2 incorporating a patient 4 in accordance with one or more techniques of this disclosure. Example techniques may be used with IMD10, which may be in wireless communication with external device 12 and at least one of the other devices not depicted in fig. 1. In some examples, IMD10 may be implanted outside of the chest of patient 4 (e.g., subcutaneously in the pectoral position illustrated in fig. 1). IMD10 may be positioned near the sternum near or just below the cardiac level of patient 4, e.g., at least partially within the cardiac contour. IMD10 includes a plurality of electrodes (not shown in fig. 1) and is configured to sense cardiac EGMs via the plurality of electrodes. In some examples, IMD10 assumes LINQ^TMForm of ICM.

The external device 12 may be a computing device having a display viewable by a user and an interface (i.e., a user input mechanism) for providing input to the external device 12. In some examples, external device 12 may be a notebook computer, a tablet computer, a workstation, one or more servers, a cellular telephone, a personal digital assistant, or another computing device that may run applications that enable the computing device to interact with IMD 10.

External device 12 is configured to communicate with IMD10 and optionally another computing device (not shown in fig. 1) via wireless communication. For example, the external device 12 may be capable of near field communication techniques (e.g., inductive coupling, NFC, or other communication techniques operable at a range of less than 10-20 cm) and far field communication techniques (e.g., according to 802.11 orRadio Frequency (RF) telemetry or other communication technologies operable at a range greater than near field communication technologies).

External device 12 may be used to configure operating parameters of IMD 10. External device 12 may be used to retrieve data from IMD 10. The retrieved data may include values of physiological parameters measured by IMD10, indications of arrhythmias or other disease episodes detected by IMD10, and physiological signals recorded by IMD 10. For example, external device 12 may retrieve a cardiac EGM segment recorded by IMD10 as IMD10 determines that a cardiac arrest or onset of another disease occurred during the segment. As will be discussed in more detail below with respect to fig. 5, one or more remote computing devices may interact with IMD10 over a network in a manner similar to external device 12, for example, to program IMD10 and/or retrieve data from IMD 10.

Processing circuitry of medical system 2, such as processing circuitry of IMD10, external device 12, and/or one or more other computing devices, may be configured to perform the example techniques of this disclosure for identifying false asystole detection. In some examples, processing circuitry of medical system 2 analyzes cardiac EGMs sensed by IMD10 and associated with the identified asystole episode to determine whether one or more of a plurality of false asystole detection criteria are satisfied. Each of the false asystole detection criteria may be configured to detect one or more indicators of noise and/or amplitude variations in the cardiac EGM. Although described in the context of an example in which IMD10 sensing cardiac EGMs includes an insertable cardiac monitor, example systems including one or more implantable or external devices of any type configured to sense cardiac EGMs may be configured to implement the techniques of this disclosure.

Fig. 2 is a functional block diagram illustrating an example configuration of IMD10 of fig. 1 according to one or more techniques described herein. In the example shown, IMD10 includes electrodes 16A and 16B (collectively, "electrodes 16"), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage 56, switching circuitry 58, and sensor 62. Although the illustrated example includes two electrodes 16, in some examples, IMDs that include or are coupled to more than two electrodes 16 may implement the techniques of this disclosure.

The processing circuitry 50 may comprise fixed function circuitry and/or programmable processing circuitry. The processing circuitry 50 may include any one or more of the following: a microprocessor, controller, Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 may include multiple components such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functionality attributed herein to processing circuitry 50 may be embodied as software, firmware, hardware, or any combination thereof.

Sensing circuitry 52 may be selectively coupled to electrodes 16 by switching circuitry 58, for example, to select electrodes 16 and polarities for sensing cardiac EGMs, referred to as sensing vectors, as controlled by processing circuitry 50. Sensing circuitry 52 may sense signals from electrodes 16, for example, to generate a cardiac EGM in order to monitor electrical activity of the heart. As an example, the sensing circuitry 52 may also monitor signals from sensors 62, which may include one or more accelerometers, pressure sensors, and/or optical sensors. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and/or sensors 62.

Sensing circuitry 52 and/or processing circuitry 50 may be configured to detect cardiac depolarization (e.g., P-wave or R-wave) when cardiac EGM amplitude crosses a sensing threshold. In some examples, the sensing threshold may 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, a sensing threshold used to detect a subsequent cardiac depolarization may decay from an initial value over a period of time. The sensing circuitry 52 and/or the processing circuitry 50 may determine an initial value based on the amplitude of the detected cardiac depolarization. The initial value and decay of the adjustable sensing threshold may be configured such that the sensing threshold is relatively high shortly after a detected cardiac depolarization when no subsequent depolarization is expected, and decays to a relatively low value over time as the occurrence of cardiac depolarizations becomes more likely. In some examples, for cardiac depolarization detection, the sensing circuitry 52 may include a rectifier, filter, amplifier, comparator, and/or analog-to-digital converter.

In some examples, sensing circuitry 52 may output an indication to processing circuitry 50 in response to the sensing of cardiac depolarization. In this manner, the processing circuitry 50 may receive detected cardiac depolarization indicators corresponding to the occurrence of detected R-waves and P-waves in respective chambers of the heart. The processing circuitry 50 may use the indications of the detected R-waves and P-waves to determine heart rate and detect arrhythmias, such as tachyarrhythmias and asystole.

The processing circuitry 50 may detect a cardiac arrest episode based on determining that the cardiac electrogram satisfies the cardiac arrest detection criteria. The asystole detection criterion may be the absence of cardiac depolarization for a threshold time period. In such examples, processing circuitry 50 may determine that the cardiac EGM satisfies the asystole detection criteria based on reaching a predetermined time interval from detection of a cardiac depolarization without receiving another cardiac depolarization indication from sensing circuitry 52.

Sensing circuitry 52 may also provide one or more digitized cardiac EGM signals to processing circuitry 50 for analysis, e.g., for cardiac rhythm differentiation, and/or for analysis to determine whether one or more false asystole detection criteria are met in accordance with the techniques of this disclosure. In some examples, based on satisfaction of asystole detection criteria, processing circuitry 50 may store a segment of the digitized cardiac EGM corresponding to a suspected asystole as episode data in storage device 56. The digitized cardiac EGM segment may contain samples of the cardiac EGM spanning a time period during which sensing circuitry 52 does not indicate detection of depolarization and a time period before and/or after the time period during which depolarization is detected. In accordance with techniques of this disclosure, processing circuitry 50 of IMD10 and/or processing circuitry of another device that retrieves episode data from IMD10 may analyze the cardiac EGM segment to determine whether one or more false asystole detection criteria are met.

Communication circuitry 54 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. The communication circuitry 54 may receive downlink telemetry from the external device 12 or another device and transmit uplink telemetry thereto by way of an internal or external antenna, such as antenna 26, under the control of the processing circuitry 50. In addition, the processing circuitry 50 may communicate with external devices (e.g., external device 12) and mayonnaise forcesA computer network such as a network communicates with networked computing devices. The antenna 26 and the communication circuitry 54 may be configured to transmit and/or receive through inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemesA signal.

In some examples, storage device 56 includes computer readable instructions that, when executed by processing circuitry 50, cause IMD10 and processing circuitry 50 to perform various functions attributed to IMD10 and processing circuitry 50 herein. Storage 56 may comprise any volatile, non-volatile, magnetic, optical, or electrical media, such as 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 an example, storage device 56 may store programmed values for one or more operating parameters of IMD10 and/or data collected by IMD10 for transmission to another device using communication circuitry 54. The data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may contain episode data for suspected cardiac arrest and/or an indication that the suspected cardiac arrest satisfies one or more false cardiac arrest detection criteria.

Fig. 3 is a conceptual side view illustrating an example configuration of IMD10 of fig. 1 and 2. In the example shown in fig. 3, IMD10 may comprise a leadless subcutaneous implantable monitoring device having a housing 15 and an insulating cover 76. Electrodes 16A and 16B may be formed or placed on the outer surface of cover 76. The circuitry 50-62 described above with respect to fig. 2 may be formed or placed on an inner surface of the cover 76 or within the housing 15. In the example shown, the antenna 26 is formed or placed on an inner surface of the cover 76, but in some examples, may be formed or placed on an outer surface. In some examples, the insulating cover 76 may be positioned over the open housing 15 such that the housing 15 and cover 76 enclose the antenna 26 and circuitry 50-62 and protect the antenna and circuitry from fluids, such as bodily fluids.

The antenna 26 or one or more of the circuitry 50-62 may be formed on the inside of the insulative cover 76, such as by using flip-chip technology. The insulating cover 76 may be flipped over onto the housing 15. When inverted and placed onto housing 15, the components of IMD10 formed on the inside of insulative cover 76 may be positioned in gap 78 defined by housing 15. The electrodes 16 may be electrically connected to the switching circuitry 58 by one or more through-holes (not shown) formed through the insulating cover 76. The insulating cover 76 may be formed from sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. The housing 15 may be formed of titanium or any other suitable material (e.g., a biocompatible material). The electrode 16 may be formed of any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, the electrodes 16 may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

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

Processing circuitry 80 may include one or more processors configured to implement functions and/or processing instructions for execution within external device 12. For example, the processing circuitry 80 may be capable of processing instructions stored in the storage device 84. The processing circuitry 80 may comprise, for example, a microprocessor, a DSP, an ASIC, an FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Thus, the processing circuitry 80 may comprise any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions attributed herein to the processing circuitry 80.

Communication circuitry 82 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from IMD10 or another device, and transmit uplink telemetry thereto. The communication circuitry 82 may 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. Communication circuitry 82 may also be configured to communicate with devices other than IMD10 via any of various forms of wired and/or wireless communication and/or networking protocols.

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

Data exchanged between external device 12 and IMD10 may contain operating parameters. External device 12 may transmit data containing computer readable instructions that, when implemented by IMD10, may control IMD10 to change one or more operating parameters and/or derive collected data. For example, processing circuitry 80 may transmit instructions to IMD10 requesting IMD10 to export collected data (e.g., asystole episode data) to external device 12. In turn, external device 12 may receive collected data from IMD10 and store the collected data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein to analyze cardiac EGMs received from IMD10, e.g., to determine whether cardiac arrest and false cardiac arrest criteria are met.

A user, such as a clinician or patient 4, may interact with the external device 12 through the 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, wherein processing circuitry 80 may present information associated with IMD10, such as cardiac EGMs, indications of detection of arrhythmia episodes, and indications of determinations that one or more false asystole detection criteria are met. Additionally, the user interface 86 may include an input mechanism configured to receive input from a user. The input mechanism may include, for example, a button, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, or a touch screen, or another input mechanism that allows a user to navigate a user interface presented by the processing circuitry 80 of the external device 12 and provide input. In other examples, the user interface 86 also includes audio circuitry for providing audible notifications, instructions, or other sounds to the user, receiving voice commands from the user, or both.

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 may be coupled with IMD10 and external device 12 via network 92, according to one or more techniques described herein. In this example, IMD10 may communicate with external device 12 via a first wireless connection and with access point 90 via a second wireless connection using communication circuitry 54. In the example of fig. 5, the access point 90, the external device 12, the server 94, and the computing device 100 are connected to each other and may communicate with each other over the network 92.

Access point 90 may comprise a device connected to network 92 through any of a variety of connections, such as a telephone dial-up, Digital Subscriber Line (DSL), or cable modem connection. In other examples, access point 90 may be coupled to network 92 through a different form of connection, including a wired connection or a wireless connection. In some instances, the access point 90 may be a user device, such as a tablet or smartphone, that may be co-located with the patient. IMD10 may be configured to transmit data, such as asystole episode data and an indication that one or more false asystole detection criteria are met, to access point 90. The access point 90 may then transmit the retrieved data to the server 94 via the network 92.

In some cases, server 94 may be configured to provide a secure storage site for data that has been collected from IMD10 and/or external device 12. In some cases, server 94 may assemble data in a web page or other document for a trained professional via computing device 100For example, by a clinician. One or more aspects of the illustrated system of fig. 5 may be used in a manner that may be similar to that described by MedtronicGeneral network technology and functionality provided by the network.

In some instances, one or more computing devices in computing 100 may be a tablet computer or other intelligent device that is co-located with a clinician, through which the clinician may program, receive alerts therefrom, and/or interrogate IMD 10. For example, a clinician may access data collected by IMD10 via computing device 100, such as when patient 4 is between clinician visits, to check the status of a medical condition. In some instances, the clinician may enter instructions for medical intervention of patient 4 into an application executed by computing device 100, such as based on a state of the patient condition determined by IMD10, external device 12, server 94, or any combination thereof, or based on other patient data known to the clinician. The device 100 may then transmit instructions for medical intervention to another one of the computing devices 100 located with the patient 4 or a caregiver to the patient 4. For example, such instructions for medical intervention may include instructions for changing drug dosage, timing or selection, for scheduling an visit with a clinician, or for seeking medical attention. In further examples, the computing device 100 may generate an alert to the patient 4 based on the state of the medical condition of the patient 4, which may enable the patient 4 to actively seek medical attention prior to receiving instructions for medical intervention. In this way, the patient 4 may be authorized to take action as needed to address his or her medical condition, which may help improve the clinical outcome of the patient 4.

In the example shown by fig. 5, server 94 includes storage 96, such as for storing data retrieved from IMD10, and processing circuitry 98. Although not shown in fig. 5, the computing device 100 may similarly include storage 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 capable of processing instructions stored in storage device 96. The processing circuitry 98 may comprise, 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. Thus, the processing circuitry 98 may comprise any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions attributed herein to the processing circuitry 98. Processing circuitry 98 of server 94 and/or processing circuitry of computing device 100 may implement any of the techniques described herein to analyze cardiac EGMs received from IMD10, e.g., to determine whether cardiac arrest and false cardiac arrest criteria are met.

Storage 96 may comprise a computer-readable storage medium or computer-readable storage device. In some examples, storage 96 includes one or more of short term memory or long term memory. Storage 96 may comprise, for example, RAM, DRAM, SRAM, magnetic disk, optical disk, 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.

Fig. 6 is a flowchart illustrating example operations for determining whether an identification of a asystole episode is false based on whether a plurality of false asystole detection criteria are met. According to the illustrated example of fig. 6, processing circuitry 50 of IMD10 determines that at least one asystole detection criterion is satisfied based on cardiac EGMs sensed by sensing circuitry 52 of IMD10 (120). For example, as discussed in more detail with respect to fig. 2, processing circuitry 50 may 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.

Based on determining that the asystole detection criteria are met, the processing circuitry 50 determines whether one or more of a plurality of false asystole detection criteria are met. In the example shown, the processing circuitry 50 determines whether false asystole detection criteria, including asystole detection count criteria, are met (122). For example, the processing circuitry 50 may determine whether the asystole detection criteria have been met at least a threshold number of times within a predetermined time period extending back from the most recent satisfaction of the asystole detection criteria, e.g., at least twice within the past thirty days. As another example, the processing circuitry may determine whether the asystole detection criteria are met at a threshold rate over a period of time, such as a rate of asystole every thirty days. The time period may be the entire time that IMD10 has been active since implantation, or since a time period other than implantation has begun, e.g., a fixed number of days, weeks, or months since implantation or at the start of a power-on reset or other reset of IMD 10.

Based on determining that the asystole detection count criteria are not met (no of 122), the example operations of fig. 6 end (124). Based on determining that the asystole detection count criteria are met (yes of 122), the processing circuitry 50 proceeds to determine whether one or more of the other false asystole detection criteria are met. Implementation of such asystole detection count criteria is based on the following observations: false asystole detection tends to occur in devices that frequently detect asystole, and not in devices that do not frequently detect cardiac activity. Requiring that the asystole detection count criteria be met before applying other asystole detection criteria, as in the example operation of fig. 6, a false classification of a suspected asystole as false by the pseudoasystole detection criteria may be avoided.

Noise signals may be present in the cardiac EGM intermittently or at varying frequencies based on, for example, changes in the condition of IMD10 or patient 4. During periods when the frequency of asystole detections (indicating that noise may be present in the EGM) is greater than periods when the frequency of asystole detections is lower, the likelihood that a given asystole detection is false (e.g., caused by noise) may be greater. Implementing the asystole detection counting criteria using the false asystole detection criteria described herein to selectively activate and deactivate the evaluation of cardiac EGMs suspected of asystole episodes may enable different emphasis on the sensitivity versus specificity of asystole detection depending on the frequency of recent asystole and, therefore, the likelihood that the recent asystole episode is false.

During periods in which the asystole frequency is below the threshold such that the asystole count criteria are not met, the processing circuitry 50 may not activate the evaluation of cardiac EGMs suspected of having asystole episodes using the false asystole detection criteria, thereby maintaining the sensitivity of asystole detection. During periods in which the asystole frequency is above the threshold such that the asystole count criteria are not met, the processing circuitry 50 may activate the evaluation of cardiac EGMs suspected of having asystole episodes using the false asystole detection criteria, thereby improving the specificity of asystole detection. Thus, selectively (e.g., intermittently) activating and deactivating an assessment of a cardiac EGM suspected of having a cardiac arrest episode using a false arrest detection criterion may provide a desired balance between sensitivity and specificity for a current condition of the cardiac EGM (e.g., a degree of noise in the cardiac EGM).

In the example shown, based on determining that the asystole detection count criteria are met (yes of 122), processing circuitry 50 proceeds to determine whether a threshold number of depolarizations that were not detected using the asystole detection criteria amplitude threshold were detected within the time interval of the cardiac EGM using a reduced amplitude threshold (126). Based on depolarizations that meet the reduced amplitude threshold criteria (yes of 126), processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on depolarizations that do not meet the reduced amplitude threshold criteria (no of 126), processing circuitry 50 may proceed to consider another false asystole detection criteria.

In the example shown, based on determining that depolarization at the reduced amplitude threshold criterion is not met (no of 126), the processing circuitry 50 proceeds to determine whether the cardiac EGM associated with meeting the asystole detection criterion also meets the attenuating noise criterion (130). Based on the attenuated noise criteria being met (yes of 130), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the attenuating noise criterion not being met (no of 130), the processing circuitry 50 may proceed to consider another false asystole detection criterion.

In the example shown, based on determining that the attenuating noise criteria are not met (no of 130), the processing circuitry 50 proceeds to determine whether the cardiac EGM associated with the suspected asystole episode meets a previous depolarization variability criteria (132). Based on the previous depolarization variability criteria being met (yes of 132), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the previous depolarization variability criterion not being met (no of 132), the processing circuitry 50 may proceed to consider another false asystole detection criterion.

In the example shown, based on determining that the previous depolarization variability criteria are not satisfied (no of 132), the processing circuitry 50 proceeds to determine whether the cardiac EGM associated with the suspected asystole episode satisfies the energy pattern criteria (134). Based on the energy pattern criteria being met (yes of 134), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the energy criteria not being met (no of 134), the example operations of fig. 6 end (124).

Based on the example operation of fig. 6 ending (124), the processing circuitry 50 may classify the suspected asystole episode as a true asystole episode, for example, because none of the false asystole detection criteria were met, or an insufficient number or combination of false asystole detection criteria were met. Based on the asystole episode being classified as authentic, the processing circuitry 50 may use the asystole episode in additional operations such as calculating statistics, determining patient condition, or transmitting actual episode data to other devices. Based on determining that the suspected asystole episode is a false asystole (128), the processing circuitry 50 may use the false asystole episode in further operations such as calculating statistics of the false episode and transmitting the false episode data to other devices, e.g., for consideration by a user to modify operation of the IMD10 to avoid further false asystole detection.

The order and flow of operations shown in fig. 6 is one example. In other examples according to the present disclosure, more or fewer false asystole detection criteria may be considered, the false asystole detection criteria may be considered in a different order, or a different number or combination of false asystole detection criteria may need to be met to determine that a suspected asystole episode is false. Further, in some examples, the processing circuitry may or may not perform the method of fig. 6 or any of the techniques described herein, e.g., by external device 12 or computing device 100, as directed by a user. For example, the patient, clinician, or other user may turn on or off the functionality for identifying false asystole detection either remotely (e.g., using Wi-Fi or cellular services) or locally (e.g., using an application provided on the patient's cellular phone or using a medical device programmer).

Additionally, although described in the context of an example in which IMD10 and processing circuitry 50 of IMD10 perform each of the portions of the example operations, the example operations of fig. 6 and the example operations described herein with respect to fig. 7-17 may be performed by any combination of one or more of any one or more of the processing circuitry of any one or more of the devices of the medical system, such as processing circuitry 50 of IMD10, 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 IMD10 may determine whether the asystole detection criteria are met and provide episode data for a suspected asystole episode to another device. In such examples, processing circuitry of other devices (e.g., external device 12, server 94, or computing device 100) may apply one or more false asystole detection criteria to the episode data.

Fig. 7 is a diagram illustrating cardiac EGM148 associated with an identified episode suspected of cardiac arrest and an example technique for determining whether an example false asystole detection criterion is satisfied based on cardiac EGM 148. In some examples, cardiac EGM148 is a digitized segment of a cardiac EGM sensed by sensing circuitry 52 of IMD10 through electrodes 16 and corresponds to a suspected asystole episode identified by processing circuitry 50 applying one or more asystole detection criteria to the cardiac EGM.

Fig. 7 illustrates cardiac depolarizations 150A-150H (in this example, R-waves) identified by IMD10, for example, by comparing cardiac EGM148 to an amplitude threshold, which may be automatically adjustable, as described herein. Fig. 7 also shows a cardiac arrest interval 152. The asystole interval 152 represents the time interval between adjacent depolarizations 150F and 150G identified by IMD 10. As described herein, processing circuitry 50 may have determined that the asystole detection criteria are met when asystole interval 152 reaches a predetermined threshold amount of time. Based on satisfaction of the asystole detection criteria, processing circuitry 50 may have stored in storage 52 cardiac EGM148 (containing time periods before and after asystole interval 152) and indications of the detection (e.g., timing) of depolarizations 150A-150H.

As described with reference to item 126 of fig. 6, one false asystole detection criterion may comprise using a reduced amplitude threshold 154 to determine whether a threshold number of depolarizations are detected within cardiac EGM148 during a asystole interval 152. In the example shown by fig. 7, processing circuitry 50 detects depolarizations 150I-150K during interval 152 by comparing cardiac EGM148 to a reduced amplitude threshold 154. The threshold number of depolarizations required to meet the reduced amplitude threshold criteria detected using threshold 154 during interval 152 can be any integer greater than or equal to one, including two or three detected depolarizations.

In some examples, the processing circuitry 50 determines the reduced amplitude threshold 154 based on the amplitude of a predetermined number of depolarizations 150A-150F prior to the asystole interval 152. In some examples, processing circuitry 50 determines the amplitude of depolarizations 150A-150F by determining the amplitude of cardiac EGM148 at samples corresponding to zero crossings in the differential signal of cardiac EGM 148. In some examples, the processing circuitry 50 determines a representative value, such as a median or average value, of the amplitudes of the depolarizations 150A-150F and determines the reduced amplitude threshold 154 to be a predetermined fraction, such as a fraction or percentage, of the representative amplitudes. As an example, the predetermined portion may be 1/10, 1/8, 1/5, 1/3, or 1/2. Any number of previous depolarizations can be used to determine the threshold 154, such as two to eight previous depolarizations, including six in some examples.

In one example, if the median amplitude of the six previous R-waves was 80 microvolts (μ V), the reduced amplitude threshold 154 of 1/8 for the median amplitude would be 10 μ V. In such an example, if a 15 μ V signal is present in cardiac EGM148 during interval 152, processing circuitry 50 will determine that the false asystole detection criteria are met. Applying a reduced amplitude threshold 154 during the asystole interval 152 may also obscure the AV block with a P-wave of 15 μ V. However, applying this false asystole detection criterion based on satisfaction of the asystole detection count criterion (122 of fig. 6) and a low probability that this false asystole detection criterion will be satisfied by cardiac EGMs having relatively high R-wave amplitudes reduces the likelihood of misclassification of the episode.

Fig. 8 is a flowchart illustrating example operations (e.g., corresponding to item 126 in fig. 6) for determining whether an example false cardiac arrest criterion is satisfied that includes a reduced amplitude threshold for depolarization detection. The example operation of fig. 8 is described with reference to cardiac EGM148 and other data illustrated in fig. 7.

According to the illustrated example of fig. 8, processing circuitry 50 of IMD10 identifies a predetermined number "N" depolarizations 150 prior to a asystole interval 152, e.g., the most recent N depolarizations prior to the asystole interval (160). Processing circuitry 50 determines the amplitudes of the N previous depolarizations 150 (162). The processing circuitry 50 determines a reduced amplitude threshold 154(164) based on the amplitudes of the N previous depolarizations 150, e.g., based on a predetermined fraction or other portion of the determined median or other representative value of the amplitudes of the N previous depolarizations 150.

Processing circuitry 50 compares the reduced amplitude threshold 154 to cardiac EGMs 148 within the asystole interval 152 (e.g., the portion of cardiac EGMs 148 within the entire asystole interval or a portion of the asystole interval) (166). Processing circuitry 50 determines whether a threshold number of depolarizations 150(168) are identified within the asystole interval 152 based on the comparison, e.g., based on cardiac EGM148 being equal to or greater than a reduced amplitude threshold 154 within the asystole interval. As an example, the threshold number of depolarizations can be one, two, or three depolarizations. Based on detecting a threshold number of depolarizations (yes of 168), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the threshold number of depolarizations not being detected (no of 168), processing circuitry 50 may proceed to apply another false asystole detection criterion, such as the attenuating noise criterion described with reference to block 130 of fig. 6 (170).

Fig. 9 is a diagram illustrating a cardiac EGM 181 that includes attenuated noise. Cardiac EGM 181 may be a digitized cardiac EGM segment containing episode data as a suspected asystole episode.

Within the time span generally indicated by bracket 182, cardiac EGM 181 contains somewhat consistent peaks and patterns of variation in amplitude that repeat at relatively consistent time intervals. During the time span generally indicated by bracket 183, cardiac EGM 181 does not continue to provide the consistent pattern previously provided during the time span indicated by bracket 182, but rather provides a large amplitude spike 184 having a much larger amplitude and duration than any of the peaks provided in cardiac EGM 181 during the time span indicated by bracket 182.

After amplitude spike 184, and during the time span generally indicated by bracket 185, cardiac EGM 181 contains greater variation in signal amplitude and may contain more negative peaks and/or lower overall average or median amplitude values than if these same parameters were measured within the time span indicated by bracket 182. In some examples of false asystole detection criteria, processing circuitry 50 may analyze amplitude spikes 184 and/or changes exhibited during a time span indicated by brackets 185 after amplitude spikes 184 to determine whether these portions of cardiac EGM 181 represent noise signals, such as attenuating noise.

Fig. 10 is a diagram illustrating a cardiac EGM 191 containing attenuated noise and an example technique for determining whether an example false asystole detection criterion is met based on the cardiac EGM. Cardiac EGM 191 may be a digitized cardiac EGM segment that is contained by IMD10 as episode data for a suspected asystole episode, e.g., based on processing circuitry 50 of IMD10 determining that asystole detection criteria are met.

As shown in fig. 10, cardiac EGM 191 contains amplitude spikes 202 in the portion of cardiac EGM 191 after the time indicated as "0" (zero) seconds. Using a set of detection windows, such as illustratively represented by detection windows 194 and 196 in the example of fig. 10, processing circuitry 50 may analyze one or more portions of cardiac EGM 191 to determine whether cardiac EGM 191 contains a noise signal, such as amplitude spikes 202 or other attenuating noise.

In various examples, analyzing cardiac EGM 191 to determine whether a noise signal is present includes determining a sample time 192 as a basis for setting detection windows 194 and 196. In some instances, determining the sampling time 192 includes setting the sampling time equal to a time where a depolarization 203, such as an R-wave, has been detected within the cardiac EGM 191. In some instances, depolarization 203 may be the most recent depolarization prior to asystole interval 152 (fig. 7).

Once the processing circuitry 50 selects the sample time 192, the processing circuitry 50 may set the baseline window 194 such that the baseline window contains a time span 195 extending from the sample time 192 and extending some amount of time before the sample time 192. The width of the time span 195 is not limited to any particular time span and may be a time span in the range of 0.5 to 5 seconds in some examples. In the example of fig. 10, the baseline window 194 extends from the sample time 192 and includes an illustrative time span 195 of approximately 1 second, extending to encompass a portion of the cardiac EGM 191 that ranges from the sample time 192 to a time at most one second prior to the sample time 192.

In various examples, processing circuitry 50 determines baseline amplitude value 199 based on sampling time 192 and baseline window 194. Processing circuitry 50 may calculate a value for baseline amplitude 199 by determining amplitudes of samples of cardiac EGM 191 that fall within baseline window 194 and determining baseline amplitude 199 based on these determined amplitude values. In some examples, the value of baseline amplitude 199 may be an average or median of the amplitude values of cardiac EGM 191 during baseline window 194.

The processing circuitry 50 also sets the measurement window 196 to encompass a time span 197 that extends from the sample time 192 and some amount of time after the sample time 192. The time span 197 is not limited to any particular duration and may be in the range of 0.5 to 5 seconds in some examples. In the example of fig. 10, the time span is about 1 second in duration, including the portion of the cardiac EGM 191 that ranges from the sample time 192 to a time at most one second after the sample time 192. In various examples, the width of the time span 197 of the measurement window 196 is equal to or different than the width of the time span 195 set for the baseline window 194. In instances in which the depolarization 203 is the most recent depolarization prior to the asystole interval 152, the measurement window 196 contains at least a portion of the asystole interval.

The processing circuitry 50 may determine amplitude values of samples of the cardiac EGM 191 within the measurement window 196. Processing circuitry 50 may determine an area under the curve value for a portion of cardiac EGM 191 based on these sampled amplitude values within measurement window 196 and a baseline amplitude value 199 determined based on baseline window 194.

For example, processing circuitry 50 may determine a set of differences between the amplitude values of cardiac EGM 191 falling within measurement window 196 and baseline amplitude value 199. In some examples, processing circuitry 50 determines the area under the curve value by calculating an area 198 contained under a portion of cardiac EGM 191 that falls within measurement window 196 and is above baseline amplitude value 199. The calculation of the area under the curve value is not limited to any particular technique for calculating this area, and may include any technique for calculating the area under the curve, as will be understood by those of ordinary skill in the art. Once the area under the curve value has been calculated for area 198, processing circuitry 50 may compare the area under the curve value to a noise signal threshold. In some examples, if the area under the curve value exceeds or equals the noise signal threshold, the processing circuitry 50 determines that a noise signal has been detected within the cardiac EGM 191 and the false asystole detection criteria are met.

Although in the example of fig. 10 the baseline window 194 extends backward in time and the measurement window 196 extends forward in time from the most recent previous depolarization 203, the processing circuitry 50 may set the baseline window 194 and the measurement window 196 to have other temporal relationships with the depolarizations 203. For example, the processing circuitry 50 may set the baseline window 194 to extend forward in time from the depolarization 203 and set the measurement window 196 to extend forward in time from the end of the baseline window 194. In such instances, baseline window 194 may correspond to a period after depolarization 203 is detected during which IMD10 is prevented from detecting subsequent depolarizations, referred to as a blanking period. In such instances, the measurement window 196 may have a greater duration than the baseline window 194, e.g., to capture the expected duration of the amplitude spike 202. In some examples, the baseline window 194 and the measurement window 196 need not be continuous or contiguous.

Typically, the P-waves are relatively narrower and/or more evenly distributed above and below the baseline amplitude, and thus have smaller area under the curve measurements, and then attenuate, e.g., exponentially attenuate, the noise signal. Thus, the area under the curve measurement may be an effective discriminator between the P-wave that occurs during true asystole and the attenuating noise that causes false asystole detection.

Fig. 11 is a flowchart illustrating example operations (e.g., corresponding to item 130 of fig. 6) for determining whether example false cardiac arrest criteria for detecting attenuating noise are met. The example operation of fig. 11 is described with reference to cardiac EGM148 and other data illustrated in fig. 7 and cardiac EGM 191 and other data illustrated in fig. 10.

According to the example of fig. 11, processing circuitry 50 identifies the last depolarization 203(220) preceding the asystole interval 152. The processing circuitry 50 further sets the baseline window 194 and the measurement window 196(222) based on the time of the last depolarization 203. Processing circuitry 50 determines baseline amplitude 199 based on the amplitude of cardiac EGM 191 within baseline window 194, e.g., as an average or median of the amplitudes within baseline window 194 (224).

Processing circuitry 50 further determines an area under the curve measurement (226) of the portion of cardiac EGM 191 within measurement window 196 relative to baseline amplitude 199. For example, the processing circuitry 50 may determine the area under the curve measurement based on a sum of the differences between the amplitudes of the samples of the cardiac EGM 191 within the measurement window 196 and the baseline amplitude 199. Any known technique for area under the curve measurement may be employed.

Processing circuitry 50 determines whether the area under the curve measurement meets a threshold, e.g., is equal to or greater than a threshold (228). Based on the area under the curve measurement satisfying the threshold (yes of 228), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the area under the curve measurement not satisfying the threshold (no of 228), the processing circuitry 50 may proceed to apply another false asystole detection criterion, such as the previous depolarization variability criterion described with reference to block 132 of fig. 6 (170).

Fig. 12 is a diagram illustrating a differential signal 241 of a cardiac EGM containing attenuated noise and an example technique for determining whether an example false asystole detection criterion is met based on the cardiac EGM. Processing circuitry 50 of IMD10 may determine differential signal 241 based on digitized cardiac EGM segments contained by IMD10 as episode data for a suspected asystole episode, e.g., based on processing circuitry 50 determining that asystole detection criteria are met. In some examples, the processing circuitry 50 determines the value of each sample "y" of the differential signal 241 by taking an amplitude value of a corresponding sample "y" of the cardiac EGM and subtracting from the amplitude value of the cardiac EGM at sample "y-n", where n is the predetermined number of samples.

As shown in fig. 12, some of the values of differential signal 241 are below the "zero" value line 237, and some of the signal values within differential signal 241 are above the "zero" value line 237. A noise signal in a cardiac EGM, such as amplitude spike 202 illustrated in fig. 10, may cause a differential signal to have one or more spikes, such as spike 242 in differential signal 241, followed by a gradual return of differential signal 241 to zero line 237.

The processing circuitry 50 may set the measurement window 246 based on the detection of events such as R-waves of other depolarizations 243 in the cardiac EGM. In the example shown, the time span 245 begins at the time that the depolarization 243 is detected. In the example of fig. 12, the time span 245 extends over a period of 0.5 seconds. The time periods contained within time span 245 are not limited to any particular time span and may range from 0.2 to 1 second in some instances. In some instances, time span 245 may correspond to a period after detecting depolarization 243 during which IMD10 is prevented from detecting subsequent depolarizations, referred to as a blanking period.

Measurement window 246 begins at the time of expiration of time span 245, as illustrated by dashed vertical line 247, and extends within time span 248, ending at the expiration of time span 248. In the example of fig. 12, the time span 248 extends over a period of 1.5 seconds. The time periods contained in time span 248 are not limited to any particular time span and may range from one second to five seconds in some instances.

Processing circuitry 50 determines the sign for the samples of differential signal 241 within measurement window 246, i.e., positive above neutral line 237, negative below neutral line 237, or on the neutral line. The processing circuitry 50 determines a count of one or more of the symbols and determines whether the count meets, e.g., equals, exceeds, or falls below a threshold. The count may take the form of a percentage or fraction of the total number of samples considered. Generally, when attenuating noise is present in the cardiac EGM, the sign of the differential signal 241 within the measurement window 246 will be unbalanced, e.g., more negative in the example of fig. 12. While negative signs may be counted or quantified in some examples, other examples may include counting or quantifying the number of positive sample values, the number of non-negative sample values (e.g., a count of zero sample values plus positive sample values), or the number of non-positive sample values (e.g., a count of zero sample values plus negative sample values).

Using the imbalance of the sign of the differential signal within the measurement window after the last depolarization to detect the presence of attenuating noise may involve a simpler calculation of the processing circuitry 50 of IMD10 and then calculating the area under the curve to detect attenuating noise. Further, P-waves or thermal noise occurring during asystole intervals during true asystole will have a substantially equal sign distribution of the differential signal within the measurement window (occurring during asystole intervals), while exponential or other attenuating noise may have more than 70% of the samples with annotated signs, e.g., negative.

Fig. 13 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria for detecting attenuating noise are met. The example operation of fig. 13 is described with reference to the cardiac differential signal 241 and other data illustrated in fig. 12.

According to the example of fig. 13, the processing circuitry 50 identifies the last depolarization 243(260) preceding a asystole interval, such as asystole interval 152 in fig. 7. The processing circuitry further sets a measurement window 246(262) beginning at a time period 245 after the last depolarization and determines a differential signal 241(264) within the measurement window 246. The processing circuitry 50 further determines the sign of the samples of the differential signal 241 within the measurement window 246 and counts or otherwise quantifies the number of samples having a sign for at least one of the signs (266).

Processing circuitry 50 determines whether the count of one of the symbols satisfies a common symbol threshold, e.g., is equal to or greater than a threshold (268). Based on the common symbol threshold being met (yes of 268), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the common symbol threshold not being met (no of 268), processing circuitry 50 may proceed to apply another false asystole detection criterion, such as the previous depolarization variability criterion described with reference to block 132 of fig. 6 (270).

Fig. 14 is a diagram illustrating cardiac EGM 290 associated with an identified asystole episode and an example technique for determining whether another example false asystole detection criterion is satisfied based on the cardiac EGM. Cardiac EGM 290 may be a digitized cardiac EGM segment containing episode data as a suspected asystole episode, e.g., based on processing circuitry 50 of IMD10 determining that asystole detection criteria are met.

Fig. 14 illustrates cardiac depolarizations 292A-292G (R-waves in this example) identified by IMD10, e.g., by comparing cardiac EGM 290 to an amplitude threshold, which may be automatically adjustable, as described herein. Fig. 14 also shows a asystole interval 294. The asystole interval 294 represents the time interval between adjacent (in time) depolarizations 292F and 292G identified by IMD 10. As described herein, processing circuitry 50 may have determined that the asystole detection criteria are met when asystole interval 294 reaches a predetermined threshold amount of time. Based on satisfaction of the asystole detection criteria, the processing circuitry may have stored in the storage device 52 indications of the cardiac EGM 290 (including time periods before and after the asystole interval 294) and the detection (e.g., timing) of depolarizations 292A-292G (collectively, "depolarizations 292").

As described with reference to item 132 of fig. 6, one false asystole detection criterion may comprise determining whether the variability of the N depolarizations 292 prior to the asystole interval 294 meets a variability threshold. The number of depolarizations "N" prior to the asystole interval 294 can be any integer greater than one, such as four, six, or eight. The N depolarizations 292 can, but need not, include the last depolarization 292F prior to the asystole interval 294. For example, the processing circuitry 50 may determine the variability of the six depolarizations 292A-292F prior to the asystole interval 292.

The variability may have an amplitude or other characteristic of depolarization 292. The processing circuitry 50 may measure or otherwise characterize the variability of the plurality of values using any known technique in order to determine the variability of the previous depolarization 292. In some examples, processing circuitry 50 compares, for example, determines the difference between the maximum amplitude and the median amplitude of the previous depolarizations. In such examples, processing circuitry 50 may determine whether the difference or other comparison satisfies, for example, exceeds a predetermined threshold.

Electrical noise can lead to false asystole detection. In some examples, cardiac EGMs with cardiac arrest episodes that are falsely detected due to electrical noise appear as flat lines with the addition of random peaks in the range of 40uV to 2000 uV. A real cardiac EGM may not contain such a wide range of R-wave amplitudes within a few seconds. Variability of previous depolarizations, such as the difference between the maximum and median, can be very sensitive and specific to false asystole detection caused by electrical noise. Although such criteria may falsely reject true asystole detection if electrical noise occurs just prior to the detection of true asystole, such fusion is unlikely to occur, particularly in IMDs with asystole detection frequencies below the asystole count detection threshold (122 of fig. 6).

FIG. 15 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria are met. The example operation of fig. 15 is described with reference to cardiac EGM 290 and other data as illustrated in fig. 14.

According to the example of fig. 15, processing circuitry 50 identifies N depolarizations 292(300) prior to a asystole interval 294. The processing circuitry 50 determines the variability of the N previous depolarizations 292. For example, the processing circuitry 50 may determine the amplitudes of the N previous depolarizations 292, determine the maximum amplitude of the N previous depolarizations 292, and determine a representative value, e.g., a median or average of the amplitudes, of the amplitudes of the N previous depolarizations 292 (302). Processing circuitry 50 further determines a comparison metric, such as a difference or ratio, between the maximum amplitude and the representative amplitude (304).

Processing circuitry 50 determines whether the comparison metric satisfies a threshold, e.g., is equal to or greater than a threshold (306). Based on the comparison metric satisfying the threshold (yes of 306), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the comparison metric not satisfying the threshold (no at 306), the processing circuitry 50 may proceed to apply another false asystole detection criterion, such as the energy pattern criterion described with reference to block 134 of fig. 6 (308).

Fig. 16 is a diagram illustrating a cardiac EGM 320 associated with an identified asystole episode and an example technique for determining whether another example false asystole detection criterion is satisfied based on the cardiac EGM. Cardiac EGM 320 may be a digitized cardiac EGM segment that is contained by IMD10 as episode data for a suspected asystole episode, e.g., based on processing circuitry 50 of IMD10 determining that asystole detection criteria are met.

Fig. 16 illustrates cardiac depolarizations 322A-322E (in this example, R-waves) identified by IMD10, such as by comparing cardiac EGM 320 to an amplitude threshold, which may be automatically adjustable, as described herein. Fig. 16 also shows a cardiac arrest interval 323. The asystole interval 323 represents the time interval between adjacent depolarizations 322D and 322E identified by IMD 10. As described herein, the processing circuitry 50 may have determined that the asystole detection criteria are met when the asystole interval 323 reaches a predetermined threshold amount of time. Based on satisfaction of the asystole detection criteria, the processing circuitry 50 may have stored in the storage device 52 indications of the cardiac EGM 320 (including the time periods before and after the asystole interval 323) and the detection (e.g., timing) of depolarizations 322A-322E (collectively "depolarizations 322").

As described with reference to item 134 of fig. 6, one false asystole detection criterion may include evaluating an energy pattern of the cardiac EGM 320 during the asystole interval 323. When a physician looks at a graphical representation of a cardiac EGM, such as cardiac EGM 320, to determine whether a suspected cardiac arrest is true or false, the physician may measure or estimate the R-R interval prior to the cardiac arrest interval 323 and determine whether there is a visible small peak within the cardiac arrest interval 323 that is the same or in phase with, for example, the velocity that would have a similar R-R interval as the previous R-R interval. Such patterns may indicate to the physician that the asystole detection is false and caused by, for example, a drop in R-wave amplitude. In contrast, during a true cardiac arrest caused by an A-V block, a small peak within the asystole interval 323 that is out of phase with the previous R-R interval may be a P-wave.

In some examples, the processing circuitry 50 identifies N depolarizations 322 prior to the asystole interval 323, such as N consecutive depolarizations 322 that include the last depolarization 322D prior to the asystole interval 323. Based on the N previous depolarizations 322, the processing circuitry 50 may determine N-1 intervals between the previous depolarizations 322, including intervals 324A and 324B (collectively "inter-depolarization intervals 324"). N may be any integer, such as seven or thirteen. Within the asystole interval 323, the processing circuitry 50 sets expected depolarization windows 326A-326C (collectively, "expected depolarization windows 326") and expected depolarization windows 328A-328C (collectively, "expected depolarization windows 328") based on the inter-depolarization interval 324. Although three of each type of window are shown in the example of fig. 16, other examples may employ more or fewer windows of each type, and/or a different number of windows for the two types of windows.

In some examples, processing circuitry 50 determines a median or other representative value for the inter-depolarization intervals 324. Processing circuitry 50 may set windows 326 and 328 within asystole interval 323 based on the representative value of the inter-depolarization interval 324. For example, the processing circuitry 50 may set each of the expected depolarization windows 326 to occur, e.g., centered in time, i.e., a different integer multiple of the representative interval after the last previous depolarization 322D. In one such example, the processing circuitry 50 may set the expected depolarization window 326A to a representative interval after depolarization 322D, set the expected depolarization window 326B to twice the representative interval after depolarization 322D, and set the expected depolarization window 326C to three times the representative interval after depolarization 322D. The processing circuitry 50 may set each of the expected inter-depolarization windows 328 to occur, e.g., centered in time, i.e., a different non-integer, e.g., fractional, multiple, of the representative interval after the last previous depolarization 322D. In one such example, processing circuitry 50 may set expected inter-depolarization window 328A to one-half of the representative interval after depolarization 322D, set expected inter-depolarization window 328B to one-half of the representative interval after depolarization 322D, and set expected inter-depolarization window 328C to two-half of the representative interval after depolarization 322D. The width of the windows 326 and 328 may be set to a predetermined portion, such as a fraction or percentage, or a representative interval, and the predetermined portion may be the same as or different from that between the windows 326 and 328.

The processing circuitry 50 determines a first energy value for an expected depolarization window 326 and a second energy value for an expected inter-depolarization window 328. In some examples, the processing circuitry 50 determines an energy value for each of the window 326 and the window 328, and then determines a first average, median, or other representative performance value for the energy values of the window 326, and a second representative performance value for the energy values of the window 328. The processing circuitry 50 may employ any known technique for determining the energy of the signal within the window. In some examples, as the energy value for each of the windows 326 and 328, the processing circuitry 50 determines a difference, ratio, or other comparative metric between the maximum amplitude of the cardiac EGM 320 and the minimum amplitude of the cardiac EGM 320 within the window.

The processing circuitry 50 further determines a difference, ratio, or other comparison metric between the first representative performance magnitude of the expected depolarization window 326 and the second representative performance magnitude of the expected inter-depolarization window 328. The processing circuitry 50 determines whether the comparison metric meets, e.g., equals or exceeds, a threshold. A relatively higher first representative performance magnitude compared to the second energy level may indicate the presence of a low amplitude depolarization, e.g., R-wave, in phase with the rhythm prior to the asystole interval 323 within the asystole interval 323, and the suspected asystole is a false asystole detection.

FIG. 17 is a flowchart illustrating another example operation for determining whether example false cardiac arrest criteria are met. The example operation of fig. 17 is described with reference to the cardiac EGM 320 and other data illustrated in fig. 16.

According to the example of fig. 17, processing circuitry 50 identifies N depolarizations 322(340) prior to a asystole interval 323. The processing circuitry 50 determines the interval 324(342) between the N previous depolarizations 322. The processing circuitry 50 further sets an expected depolarization window 326 and an expected depolarization window 328(344) within the asystole interval 323 based on the interval 324, e.g., based on integer and non-integer multiples of the mean or median, respectively, of the interval 124.

The processing circuitry 50 determines respective energy values for the expected depolarization window 326 and the expected inter-depolarization window 328, e.g., the difference between the maximum amplitude and the minimum amplitude of the cardiac EGM 320 within each window (346). The processing circuitry 50 further determines a comparison metric between the energy of the window 326 and the energy of the window 328 (348). For example, the processing circuitry 50 may determine a difference between the average of the energy of the window 326 and the average of the energy of the window 328.

Processing circuitry 50 determines whether the comparison metric satisfies a threshold, e.g., is equal to or greater than a threshold (350). Based on the comparison metric satisfying the threshold (yes of 350), the processing circuitry 50 may determine that the suspected asystole episode is false asystole (128). Based on the comparison metric not satisfying the threshold (no of 350), the processing circuitry 50 may proceed to apply another false asystole detection criterion, or, if there is no other false asystole detection criterion to apply, the operations of fig. 17 and 6 may end (124).

Fig. 18A-18C are conceptual diagrams of another example medical system 410 implanted in a patient 408. Fig. 1A is a front view of a medical system 410 implanted in a patient 408. Fig. 1B is a side view of a medical system 410 implanted in a patient 408. Fig. 1C is a lateral view of a medical device system 410 implanted in a patient 408.

In some examples, medical system 410 is an extravascular implantable cardioverter-defibrillator (EV-ICD) system implanted within patient 408. Medical system 410 includes an IMD412, which in the illustrated example is implanted subcutaneously or submuscularly on the left mid-axilla of patient 408, such that IMD412 may be positioned above the left ribcage of patient 408. In some other examples, IMD412 may be implanted at other subcutaneous locations on patient 408, such as at a thoracic location or an abdominal location. IMD412 includes a housing 420 that may form a hermetic seal that protects the components of IMD 412. In some examples, housing 420 of IMD412 may be formed from a conductive material such as titanium or from a combination of conductive and non-conductive materials that may serve as housing electrodes. IMD412 may also include a connector assembly (also referred to as a connector block or plug) that includes electrical feedthroughs through which electrical connections are made between leads 422 and electronic components contained within the housing.

IMD412 may provide cardiac EGM sensing, asystole detection, and other functions described herein with respect to IMD10, and housing 420 may house circuitry 50-62 and antenna 26 (fig. 2 and 3) that provide such functions. The housing 420 may also house therapy delivery circuitry configured to generate therapeutic electrical signals for delivery to the patient 408, such as cardiac pacing and anti-tachyarrhythmia shocks. System 410 may contain an external device 12 that may work with IMD412, as described herein with respect to IMD10 and system 2.

In the example shown, IMD412 is connected to 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 IMD412 and a distal portion including electrodes 432A, 432B, 434A, and 434B. Leads 422 extend subcutaneously over the ribcage from IMD412 toward the center of the torso of patient 408. At a location near the center of the torso, the lead 422 bends or turns and extends upward within the chest cavity below/below the sternum 424. Thus, lead 422 may be at least partially implanted in the substernal space, such as at the thoracic cage or target site between sternum 424 and heart 418. In one such configuration, a proximal portion of lead 422 may be configured to extend subcutaneously from IMD 12 toward sternum 24, and a distal portion of lead 22 may be configured to extend upward in anterior mediastinum 426 (fig. 1C) below or inferior to sternum 424.

For example, the lead 422 may extend superiorly within the chest cavity below/inferior to the sternum 424 within the anterior mediastinum 426. Anterior mediastinum 426 can be considered to be bounded posteriorly by pericardium 416, laterally by pleura 428, and anteriorly by sternum 424. In some examples, the anterior wall of the anterior mediastinum 426 can also be formed by the transverse pectoralis muscle and one or more costal cartilage. Anterior mediastinum 426 contains a quantity of loose connective tissue (e.g., cellulite), some lymphatic vessels, lymph glands, substernal muscle tissue (e.g., transverse pectoralis muscle), and small blood vessels or blood vessel branches. In one example, the distal portion of lead 422 may be implanted substantially within the loose connective tissue of anterior mediastinum 426 and/or substernal musculature. In such instances, the distal portion of the lead 422 may be physically isolated from the pericardium 416 of the heart 418. A lead that is implanted substantially within anterior mediastinum 426 is an example of a substernal lead or, more generally, an extravascular lead.

The distal portion of the lead 422 is described herein as being implanted substantially within the anterior mediastinum 426. Thus, some of the distal portions of the leads 422 may extend beyond the anterior mediastinum 426 (e.g., the proximal ends of the distal portions), but a majority of the distal portions may be positioned within the anterior mediastinum 426. In other embodiments, the distal portion of the lead 422 may be implanted intrathoracic in other non-vascular extra-pericardial locations, including gaps, tissues, or other anatomical features that surround and are adjacent to, but not attached to, the pericardium 416 or other portion of the heart 418 and are not above the sternum 424 or ribcage. Lead 422 may be implanted anywhere within the "substernal space" defined by the sternum and/or inferior surface between the ribcage and the body cavity, but does not include pericardium 416 or other portions of heart 418. The substernal space may alternatively be referred to by the terms "posterior sternal space" or "mediastinum" or "substernal" as known to those of skill in the art and includes anterior mediastinum 426. The substernal space may also contain the anatomical regions described in the following as lare space: baudoin, y.p. et al, entitled "superior aorta does not cross The lare space (trigonal rib) (The superior approach not pass through The deep larry's space (trigonum sternocostal))" (surgical and radiological anatomy (surg. radio. antat.) (25.3-4) (2003) -259-62. In other words, the distal portion of the lead 422 may 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 may be physically isolated from the pericardium 416.

Lead 422 may include an insulated lead body having a proximal end including a connector 430 configured to connect with IMD412 and a distal portion including one or more electrodes. As shown in fig. 18A, one or more electrodes of the lead 422 may include electrodes 432A, 432B, 434A, and 434B, but in other examples, the lead 422 may include more or fewer electrodes. Lead 422 also includes one or more conductors that form conductive paths within the lead body and interconnect the electrical connectors with respective ones of the electrodes.

The electrodes 432A, 432B may be defibrillation electrodes (individually or collectively, "defibrillation electrodes 432"). Although electrode 432 may be referred to herein as "defibrillation electrode 432," electrode 432 may be configured to deliver other types of anti-tachyarrhythmia shocks, such as cardioversion shocks. Although the defibrillation electrode 432 is depicted in fig. 18A-18C as a coil electrode for clarity, it should be understood that the defibrillation electrode 432 may be other configurations in other examples. Defibrillation electrode 432 may 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 below sternum 424.

Lead 422 may be implanted at a target site below or along sternum 424 such that the therapy vector substantially spans the ventricles of heart 418. In some examples, therapy vectors (e.g., shock vectors for delivering anti-tachyarrhythmia shocks) may be between defibrillation electrode 432 and a housing electrode formed by or on IMD 412. In one example, a therapy vector may be viewed as a line that extends from a point on defibrillation electrodes 432 (e.g., the center of one of defibrillation electrodes 432) to a point on a housing electrode of IMD 412. As such, it may be advantageous to increase the amount of area in which defibrillation electrode 432 (and in which the distal portion of lead 422) extends across heart 418. Accordingly, the lead 422 may be configured to define a curved distal portion as depicted in fig. 18A. In some instances, the curved distal portion of lead 22 may help improve the efficacy and/or efficiency of pacing, sensing, and/or defibrillation of heart 418 by IMD 412.

Electrodes 434A, 434B may be pacing/sensing electrodes (individually or collectively, "pacing/sensing electrodes 434") positioned on a distal portion of lead 422. Electrode 434 is referred to herein as a pace/sense electrode because it is typically configured for delivery of pacing pulses and/or sensing of cardiac electrical signals. In some cases, electrodes 434 may provide only pacing functionality, only sensing functionality, or both pacing and sensing functionality. In the example shown in fig. 18A and 18B, pace/sense electrodes 434 are spaced apart from one another by defibrillation electrode 432B. However, in other examples, pace/sense electrodes 434 may both be distal to defibrillation electrode 432B or both be proximal to defibrillation electrode 432B. In examples where the lead 422 includes more or fewer electrodes 432, 434, such electrodes may be positioned at other locations on the lead 422.

In the example of fig. 18A, the distal portion of the lead 422 is a serpentine shape that includes two "C" shaped curves, which together may resemble the greek letter epsilon, "epsilon". Defibrillation electrodes 432 are each carried by one of two respective C-shaped portions of the distal portion of the lead body. The two C-shaped curves extend or curve away from the central axis of the lead body in the same direction. In some examples, pace/sense electrode 434 may be approximately aligned with a central axis of the straight proximal portion of lead 422. In such instances, the midpoint of defibrillation electrode 432 is laterally offset from pace/sense electrode 434. Other examples of extravascular leads including one or more defibrillation electrodes and one or more pace/sense electrodes 434 carried by a curved, serpentine, undulating, or zig-zag distal portion of lead 422 may also be implemented using the techniques described herein. In some examples, the distal portion of the lead 422 can be straight (e.g., straight or nearly straight).

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

In some examples, the elongate lead body of the lead 422 can include one or more elongate electrical conductors (not shown) that extend within the lead body from a connector at the proximal lead end to electrodes 432, 434 located along a distal portion of the lead 422. One or more elongate electrical conductors contained within the lead body of the lead 422 can engage respective ones of the electrodes 432, 434. The conductors may be electrically coupled to circuitry of IMD412, such as therapy delivery circuitry and sensing circuitry 52, through connections in the connector assembly. Electrical conductors transmit therapy from the therapy delivery circuitry to one or more of the electrodes 432, 434 and transmit sensed cardiac EGMs from one or more of the electrodes 432, 434 to sensing circuitry 52 within IMD 412.

In general, IMD412 may sense cardiac EGMs, such as by one or more sensing vectors that include a combination of pace/sense electrodes 434 and/or housing electrodes of IMD 412. In some examples, IMD412 may sense the EGM using a sensing vector that includes one or both of defibrillation electrodes 432 and/or one of defibrillation electrodes 432 and one of pace/sense electrodes 434 or a housing electrode of IMD 412. The medical system 410, including the processing circuitry of the IMD412 and/or the external device 12, may perform any of the techniques described herein for determining whether asystole detection and false asystole detection criteria are met, e.g., based on cardiac EGMs sensed through the extravascular electrodes 432, 434. Cardiac EGMs sensed through extravascular electrodes may contain noise, for example, due to contact with tissue and/or changes in orientation relative to the heart, in a manner similar to that described herein with respect to subcutaneous electrodes. Typically, when the electrodes are not directly fixed to the myocardium, motion such as respiratory motion can cause changes in depolarization amplitude and other noise that may lead to false asystole detection. The techniques described herein may be implemented with cardiac EGMs sensed by subcutaneous, skin, substernal, extravascular, intramuscular, or any electrode positioned in (or in contact with) any tissue of a patient.

The techniques described in this disclosure may be implemented at least in part in hardware, software, firmware, or any combination thereof. For example, various aspects of these techniques may be implemented in 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 an external device (such as a physician or patient programmer, simulator, or other device). The terms "processor" and "processing circuitry" may generally refer to any of the foregoing logic circuitry alone or in combination with other logic circuitry or any other equivalent circuitry alone or in combination with other digital or analog circuitry.

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

Additional items are described below:

item 1. a method, comprising:

sensing a cardiac electrogram of a patient by a plurality of electrodes of a medical system; determining, by processing circuitry of the medical system, that a cardiac arrest detection criterion is met based on the cardiac electrogram; determining, by the processing circuitry, based on the cardiac electrogram signal, that at least one false asystole detection criterion of a plurality of false asystole detection criteria is satisfied based on the determining that the asystole detection is satisfied; and is

Rejecting, by the processing circuitry, an indication of a cardiac arrest episode for the patient based on determining that at least one of the plurality of false cardiac arrest detection criteria is satisfied, wherein the plurality of false cardiac arrest detection criteria comprises:

a first false asystole detection criterion that includes a reduced amplitude threshold for detecting cardiac depolarization in the cardiac electrogram; and

a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

Item 2. the method of item 1, wherein determining that the asystole detection criteria are met comprises determining that the asystole detection criteria are met based on no cardiac depolarizations being identified in the cardiac electrogram during a time interval.

Item 3. the method of item 1 or 2, wherein determining that the first false asystole detection criteria are met comprises: comparing the reduced amplitude threshold to the cardiac electrogram over the time interval; and determining, based on the comparison, that a threshold number of cardiac depolarizations were identified in the cardiac electrogram during the time interval.

Item 4. the method of any of the preceding items, further comprising, by the processing circuitry: identifying one or more cardiac depolarizations occurring in the cardiac electrogram prior to the time interval; determining an amplitude of each of the one or more identified cardiac depolarizations; and determine the reduced amplitude threshold based on the determined amplitudes of the one or more identified cardiac depolarizations.

Item 5. the method of item 4, wherein the one or more identified cardiac depolarizations includes a plurality of identified cardiac depolarizations, and determining the reduced amplitude threshold comprises: determining a representative amplitude based on the amplitude of each of the plurality of identified cardiac depolarizations; and is

Determining the reduced amplitude threshold as a predetermined fraction of the representative amplitude.

Item 6. the method of items 1, 2, 3, or 4, wherein determining that the second false asystole detection criteria are met comprises: calculating an area under the curve value of the cardiac electrogram during at least a portion of the time period; and determining that the area under the curve value satisfies an area under the curve threshold.

Item 7. the method of items 1, 2, 3, or 4, wherein determining that the second false asystole detection criteria are met comprises:

determining a differential signal of the cardiac electrogram during at least a portion of the time period; for each of a plurality of samples of the differential signal, determining whether a sign of the sample is positive or negative; and determining that an amount of samples having one of the symbols satisfies a common symbol threshold.

Item 8. the method of item 1, 2, 3, or 4, wherein the plurality of false asystole detection criteria further comprises a third false asystole detection criteria, wherein determining that the third false asystole detection criteria is met comprises: identifying a plurality of cardiac depolarizations that occurred in the cardiac electrogram prior to the time interval; determining an amplitude of each of the plurality of identified cardiac depolarizations; determining a variability of the amplitude; and determining that the variability satisfies a variability threshold.

Item 8. the method of item 8, wherein the variability of the amplitude comprises: determining a maximum amplitude of the plurality of amplitudes; determining a representative amplitude of the plurality of amplitudes; and determining a comparison measure of the maximum amplitude and the representative amplitude.

Item 9. the method of item 1, 2, 3, or 4, wherein the plurality of false asystole detection criteria further comprises a third false asystole detection criteria, and determining that the third false asystole detection criteria is met comprises:

identifying a plurality of cardiac depolarizations that occurred in the cardiac electrogram prior to the time interval; determining one or more intervals between adjacent ones of the plurality of cardiac depolarizations; based on the determined interval, identifying one or more expected cardiac depolarization windows and one or more expected inter-depolarization windows within the time interval; determining a first energy of the one or more cardiac depolarization windows and a second energy of the one or more inter-depolarization windows; determining a comparative measure of the first energy and the second energy; and determining that the comparison metric satisfies a threshold.

Item 10. the method of items 1, 2, 3, or 4, further comprising, by the processing circuitry: determining an instance count that satisfies the asystole detection criteria over a period of time; and determining whether the count satisfies at least one asystole count criterion, wherein determining whether the plurality of false asystole detection criteria are satisfied comprises determining whether the plurality of false asystole detection criteria are satisfied based on determining that the count satisfies the at least one asystole count criterion.

The method of claim 14, wherein the plurality of electrodes are implanted subcutaneously and sensing the cardiac electrogram comprises sensing the cardiac electrogram through the plurality of subcutaneously implanted electrodes.

Item 12. the method of items 1, 2, 3, or 4, wherein the plurality of electrodes are implanted extravascularly and sensing the cardiac electrogram comprises sensing the cardiac electrogram through the plurality of extravascularly implanted electrodes.

A non-transitory computer-readable storage medium comprising program instructions that, when executed by processing circuitry of a medical system, cause the processing circuitry to: determining that asystole detection criteria are met based on cardiac electrograms sensed by a plurality of electrodes of the medical system; determining whether a plurality of false asystole detection criteria are met based on the cardiac electrogram signal based on the determination that the asystole detection is met; and withhold the indication of the onset of cardiac arrest for the patient based on determining that at least one of the plurality of false cardiac arrest detection criteria is met, wherein the plurality of false cardiac arrest detection criteria comprises: a first false asystole detection criterion that includes a reduced amplitude threshold for detecting cardiac depolarization in the cardiac electrogram; and a second false asystole detection criterion for detecting attenuating noise in the cardiac electrogram.

In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Furthermore, the techniques may be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including IMDs, external programmers, combinations of IMDs and external programmers, Integrated Circuits (ICs) or collections of ICs, and/or discrete circuitry residing in IMDs and/or external programmers.