Operation of a medical device system to determine window positions of a heart sound beat signal to extract heart sound features

Abstract

An example system includes an insertable cardiac monitor (ICM) includes a housing configured for subcutaneous implantation within a patient; and a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a remote computing device; and processing circuitry of at least one of the ICM or the remote computing device, the processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

Description

OPERATION OF A MEDICAL DEVICE SYSTEM TO DETERMINE WINDOW POSITIONS OF A HEART SOUND BEAT SIGNAL TO EXTRACT HEARTSOUND FEATURES

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 593,125, filed October 25, 2023, the entire content of which is incorporated herein by reference.FIELD

[0002] The disclosure relates generally to medical device systems and, more particularly, medical device systems configured to determine or extract heart sound features in a heart sound beat signal.BACKGROUND

[0003] Medical devices may be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense cardiac electrogram (EGM) signals indicative of the electrical activity of the heart via electrodes. Some medical devices are additionally or alternatively configured to sense other signals, such as heart sound signals indicative of the mechanical activity of the heart via a motion or vibration sensor, such as an accelerometer or microphone. Some medical devices may be configured to deliver a therapy in conjunction with or separate from the monitoring of physiological signals.SUMMARY

[0004] In general, this disclosure is directed to techniques for determining windows of a heart sound beat signal to extract heart sound features. Processing circuitry may implement the techniques to extract heart sound features and, based on the features, evaluate the cardiac health of a patient, e.g., determine a degree of heart failure (HF) or likelihood of a HF decompensation, hospitalization, or other event, based on the features. In some examples, the processing circuitry determines a TR interval (interval from T-wave to next R-wave) of a cardiac EGM signal, e.g., electrocardiogram (ECG) signal, sensed by a medical device, corresponding to a heart sound beat signal, sensed by a medical device (e.g., the same medical device), and determines a position of one or more of an S2window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed cardiac EGM. The processing circuitry may extract heart sound features in the one or more of the S2, S3, or S4 window of the heart sound beat signal.

[0005] In one example technique of this disclosure, an implantable medical device (IMD) may include a plurality of electrodes to sense a cardiac EGM of a patient and may further include an accelerometer or other motion / vibration sensor to sense heart sound beat signals of the patient by sensing the cardiac vibrations. Heart sound features in a heart sound beat signal may include one or more HF specific heart sound features, such as one or more heart sound features indicative of a HF status. For example, variations in heart sound features in particular windows, such as an S2 or S3 window, over a period of time, such as hours, days, weeks, or months, may indicate a status of HF, such as a change in HF status (e.g., improving or worsening HF).

[0006] However, an RR interval of a patient may change by a small amount from beat to beat, which may cause the particular windows for each of the S 1-S4 heart sounds of each beat to not naturally align, which may increase difficulty in identifying what particular window that the cardiac sound features are located.

[0007] Determining a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal and determining a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM may help improve the accuracy of the positioning of the S2 window, S3 window, and / or S4 window of the heart sound beat signal by providing an adaptive approach of determining a position of one or more of an S2 window, S3 window, or S4 window that adapts to changes in an RR interval as the TR interval gets adapted to changes in the RR interval. This in turn helps improve the accuracy, consistency, and / or clinical value of cardiac features extracted from particular windows of the heart sound beat signal, which helps the accuracy of determining a HF status (e.g., HF score, HF classification, HF progression, and / or HF recovery) of the patient. In addition, the techniques of this disclosure may improve the alignment of the particular windows for beats taken at different times, which helps improve the comparison of heart sound features extracted from particular windows of the heart sound beat signal at a certain time to cardiac features extracted from a corresponding respective window at a later time. Suchcomparisons over time may help improve the accuracy of determining HF status (e.g., HF score, HF classification, HF progression, and / or HF recovery) of the patient.

[0008] Additionally, as the techniques and systems of this disclosure may be completely or partially implemented in an IMD that can continuously and / or periodically sense cardiac EGM and / or cardiac vibrations without human intervention while subcutaneously implanted in a patient over weeks, to determine a HF status. Using techniques of this disclosure in an IMD may be advantageous when a physician cannot be continuously present with the patient 24 hours of a day and / or be continuously present over weeks or months to evaluate cardiac EGM and heart sound signals and / or heart sound beat signals where performing millions of operations on weeks or months of cardiac EGM data and heart sound beat signal data could not practically be performed in the mind of a physician with techniques of this disclosure. For example, heart sounds may differ during the day versus at night, and may differ in ambulatory settings versus at rest.

[0009] In some examples, using techniques as described in this disclosure may result in higher specificity and sensitivity of determining HF status based on heart sound features. This higher specificity and sensitivity may increase reliability of another device, user, and / or clinician on the accuracy of determining HF status based on heart sound features. In some examples, this improved reliability on the accuracy of determining HF status based on cardiac heart sound features may result in improved usefulness of the system or computing device as a clinician, user, or other computing device may not use and / or rely upon determinations that are not at or above a specificity and sensitivity threshold. With more accurate HF status determinations provided with techniques of this disclosure, physicians and caregivers may also provide better-tailored care, therapies, and interventions for the patient experiencing HF, such as in an ambulatory setting.

[0010] In one example, this disclosure describes a system comprising: an insertable cardiac monitor (ICM) comprising: a housing configured for subcutaneous implantation within a patient; and a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a remote computing device; and processing circuitry of at least one of the ICM or the remote computing device, the processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of theheart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0011] In another example, this disclosure describes a computing device comprising: a memory; and processing circuitry coupled to the memory, the processing circuitry being configured to: receive a sensed cardiac electrogram (EGM)) and a sensed heart sound beat signal of a patient; determine a TR interval of the sensed EGM corresponding to the heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0012] In another example, this disclosure describes an insertable cardiac monitor (ICM) comprising: a housing configured for subcutaneous implantation within a patient; a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a heart sound sensor within the housing, wherein the ICM is configured to sense a heart sound beat signal via the heart sound sensor; and processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to the heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0013] In another example, this disclosure describes a method comprising: receiving a sensed cardiac electrogram (EGM)) and a sensed heart sound beat signal of a patient; determining a TR interval of the sensed EGM corresponding to the heart sound beat signal; and determining a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0014] The summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] FIG. 1 illustrates the environment of an example medical system in conjunction with a patient.

[0016] FIG. 2 is a functional block diagram illustrating an example configuration of the implantable medical device (IMD) of the medical system of FIG. 1.

[0017] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1 and 2.

[0018] FIG. 4A is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-3.

[0019] FIG. 4B is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-3.

[0020] FIG. 5 is a functional block diagram illustrating an example configuration of the external device of FIGS. 1-4.

[0021] FIG. 6 is a block diagram illustrating an example system that includes an access point, a network, external computing devices, such as a server, and one or more other computing devices, which may be coupled to the IMD and external device of FIGS. 1-5.

[0022] FIG. 7 is graphs illustrating windows of a heart sound beat signal, in accordance with some examples of the current disclosure.

[0023] FIG. 8 is a flow diagram illustrating an example technique for operating a system to output an indication of HF status based on extracted cardiac heart sound features.

[0024] FIG. 9 is a conceptual diagram illustrating an example training process for an artificial intelligence model, in accordance with examples of the current disclosure.

[0025] FIG. 10 is a conceptual diagram illustrating an example ML model configured to determine a risk level of a health event based on physiological parameter values of a patient.

[0026] Like reference characters denote like elements throughout the description and figures.DETAILED DESCRIPTION

[0027] A variety of types of medical devices sense cardiac EGMs. In some examples, EGMs may include electrocardiograms (ECGs or EKGs). Some medical devices that sense cardiac EGMs are non-invasive, e.g., using a plurality of electrodes placed in contact with external portions of the patient, such as at various locations on the skin of the patient. The electrodes used to monitor the cardiac EGM in these non-invasive processes may be attached to the patient using an adhesive, strap, belt, or vest, as examples, 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 the electrical activity of the heart or other cardiac tissue of the patient, and to provide these sensed electrical signals to the electronic device for further processing and / or display of the electrical signals. The non-invasive devices and methods may be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a doctor’ s appointment, or for example for a predetermined period of time, for example for one day (twenty-four hours), or for a period of several days.

[0028] External devices that may be used to non-invasively sense and monitor cardiac EGMs include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, or necklaces. One example of a wearable physiological monitor configured to sense a cardiac EGM is the SEEQ™ Mobile Cardiac Telemetry System, available from Medtronic, Inc., of Minneapolis, Minnesota. Such external devices may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

[0029] Some IMDs also sense and monitor cardiac EGMs. The electrodes used by IMDs to sense cardiac EGMs are typically integrated with a housing of the IMD and / or coupled to the IMD via 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, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. An example of pacemaker configured for intracardiac implantation is the Micra™ Transcatheter Pacing System, available from Medtronic, Inc. Some IMDs that do not provide therapy, e.g., implantable patient monitors, sense cardiac EGMs. One example of such an IMD is the Reveal LINQ™ and LINQ II™ Insertable Cardiac Monitors (ICMs), available fromMedtronic, Inc, which may be inserted subcutaneously. Such IMDs may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

[0030] Any medical device configured to sense a cardiac EGM via implanted or external electrodes, including the examples identified herein, may implement the techniques of this disclosure for evaluating a cardiac EGM to determine a position of one or more of an S2 window, S3 window, or S4 window of a heart sound beat signal and determine an indication of heart failure (HF) based on one or more features in one or more of the S2, S3, or S4 window. For example, circuitry of at least one of an IMD or a remote computing device may determine a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed cardiac electrogram. Circuitry may extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of a heart sound beat signal. For example, circuitry may extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of an ensemble average heart sound beat signal. In some examples, circuitry may determine an indication of HF of the patient based on the extracted heart sound features in the one or more of the S2, S3, or S4 window of the heart sound beat signal, such as based on an ensemble average heart sound beat signal. In some examples, an indication of HF may include as an amount of HF, a level of HF, a change in a level / amount of HF, such as worsening HF status or improving HF status, other quantification of HF, e.g., such as over a time period since the last HF determination, or risk of a future HF decompensation, hospitalization, or other HF event. In some examples, circuitry may output an indication of HF of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the heart sound beat signal, such as an ensemble average heart sound beat signal.

[0031] The techniques of this disclosure for determining a position of one or more of an S2, S3, or S4 window of the heart sound beat signal to determine indications of HF, such as improving or worsening HF, helps improve HF diagnostic specificity. The techniques of this disclosure for improving HF diagnostic specificity may facilitateimproved determinations of HF risk, HF burden, cardiac wellness, and risk of sudden cardiac death, and may lead to clinical interventions to suppress HF, such as medications and ablations, based on the determined indications of HF.

[0032] FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. The example techniques may be used with an IMD 10, which may be in wireless communication with at least one of external device 12 and other devices not pictured in FIG. 1. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette. IMD 10 includes a plurality of electrodes (not shown in FIG. 1), and is configured to sense a cardiac EGM via the plurality of electrodes. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II ICM™, or another ICM similar to, e.g., a version or modification of, the LINQ™ ICMs.

[0033] External device 12 may be a computing device with a display viewable by the user and an interface for providing input to external device 12 (i.e., a user input mechanism). In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or more servers, cellular phone, smartphone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, another computing device (not illustrated in FIG. 1), via wireless communication. External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far-field communication technologies (e.g., RF telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0034] In some examples, external device 12 may be or additionally include wearable computing device. A wearable computing device Imay include electrodes and other sensors to sense physiological signals of patient 4, and may collect and store physiological data and detect episodes based on such signals. Wearable computing device may be incorporated into the apparel of patient 4, such as within clothing, shoes, eyeglasses, awatch or wristband, a hat, etc. In some examples, a wearable device may be a smartwatch or other accessory or peripheral for external device 12, for example when external device 12 is a smartphone or tablet.

[0035] External device 12 may be used to configure operational parameters for 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 IMD 10, indications of HF or other maladies detected by IMD 10, and physiological signals recorded by IMD 10. For example, external device 12 may retrieve information related to HF from IMD 10, such as a level of HF, a change in a level of HF, such as worsening HF status or improving HF status, or other quantification of HF, e.g., over a time period since the last retrieval of information by external device. In some examples, external device 12 may determine such information related to HF based on physiological parameters / signals and / or other data retrieved from IMD 10. External device 12 may also retrieve cardiac EGM segments and / or heart sound beat signal segments recorded by IMD 10, e.g., according to a schedule, due to IMD 10 determining that an episode of arrhythmia or another malady or event occurred during the segment, or in response to a request to record the segment from patient 4 or another user. As discussed in greater detail below with respect to FIG. 6, one or more remote computing devices may interact with IMD 10 in a manner similar to external device 12, e.g., to program IMD 10 and / or retrieve data from IMD 10, via a network.

[0036] Processing circuitry of medical system 2, e.g., of IMD 10, external device 12, and / or of one or more other computing devices, may be configured to perform the example techniques of this disclosure for determining a TR interval of a sensed cardiac EGM corresponding to a heart sound beat signal and determining a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed cardiac EGM. In some examples, processing circuitry of medical system 2 may be configured to perform the example techniques of this disclosure to extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of an ensemble average heart sound beat signal and output an indication of HF of the patient based on the extracted heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

[0037] In some examples, IMD 10 may include an accelerometer within the housing configured to sense cardiac vibrations of the patient. In some examples, IMD 10 determines positions / times of a plurality of windows of the heart sound beat signal, including an SI window, an S2 window, an S3 window, and an S4 window. In some examples, the one or more heart sound features are based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window, S3 window, or S4 window.

[0038] In some examples, the processing circuitry of medical system 2 may extract heart sound beat signals of the patient based on sensed cardiac vibrations and generate the extracted heart sound beat signals corresponding to a respective cardiac beat signal in the sensed cardiac EGM. In some examples, the processing circuitry of medical system 2 may generate a plurality of filtered heart sound beat signals by filtering, using the sensed cardiac EGM, the extracted heart sound beat signals corresponding to a respective cardiac beat signal in the sensed cardiac EGM. In some examples, the processing circuitry of medical system 2 may generate the ensemble average heart sound beat signal using the plurality of filtered heart sound beat signals.

[0039] Although described in the context of examples in which IMD 10 that senses the cardiac EGM signal and the heart sound beat signal comprises an insertable cardiac monitor, example systems including one or more implantable or external devices of any type configured to sense a cardiac EGM signal and / or heart sound beat signal may be configured to implement the techniques of this disclosure.

[0040] FIG. 2 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 16A and 16B (collectively “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, sensors 62, and heart sound sensor(s) 62A. Although the illustrated example includes two electrodes 16, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure in some examples.

[0041] Processing circuitry 50 may include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discreteor analog logic circuitry. In some examples, processing circuitry 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof.

[0042] Sensing circuitry 52 may be selectively coupled to electrodes 16 via switching circuitry 58, e.g., to select the electrodes 16 and polarity, referred to as the sensing vector, used to sense a cardiac EGM, as controlled by processing circuitry 50. Sensing circuitry 52 may sense signals from electrodes 16, e.g., to produce a cardiac EGM, in order to facilitate monitoring the electrical activity of the heart. Sensing circuitry 52 may include components / modules for converting the raw EGM signal to a processed EGM signal that can be analyzed to detect sense events. Sensing circuitry 52 also may monitor signals from sensors 62, such as heart sound sensor(s) 62A. In some examples, heart sound sensor(s) 62A may be configured to sense cardiac vibrations. In some examples, heart sound sensor(s) 62A may include one or more accelerometers. In some examples, heart sound sensor(s) 62A may additionally or alternatively include one or more microphones and / or other vibration / motion sensors. Sensing circuitry 52 may receive raw cardiac vibrations monitored by heart sound sensors 62A. Sensing circuitry 52 may include components / modules for converting the raw cardiac vibrations to a processed heart sound beat signal that can be analyzed to detect sense events. In some examples, processing circuitry 50 may use signals received from sensors 62, including heart sound beat signals sensed by heart sound sensor(s) 62A, in addition to a sensed cardiac EGM signals to determine a HF status. 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.

[0043] Sensing circuitry 52 and / or processing circuitry 50 may be configured to detect cardiac depolarizations (e.g., P-waves of atrial depolarizations or R-waves of ventricular depolarizations) when the cardiac EGM amplitude crosses a sensing threshold. A cardiac EGM may include P-waves (depolarization of the atria), R-waves (depolarization of the ventricles), and T-waves (repolarization of the ventricles), among other events. Sensing circuitry 52 and / or processing circuitry 50 may be configured to detect one or more features of the P-waves, R-waves, and / or T-waves in a cardiac EGM. For cardiacdepolarization detection, sensing circuitry 52 may include a rectifier, filter, amplifier, comparator, and / or analog-to-digital converter, in some examples. In some examples, sensing circuitry 52 may output an indication to processing circuitry 50 in response to sensing of a cardiac depolarization. In this manner, processing circuitry 50 may receive detected cardiac depolarization indicators corresponding to the occurrence of detected R-waves and P-waves in the respective chambers of heart. Processing circuitry 50 may use the indications of detected R-waves and P-waves for determining inter-depolarization intervals, heart rate, and detecting arrhythmias, such as tachyarrhythmias and asystole.

[0044] Sensing circuitry 52 may also provide one or more digitized EGM signals and / or heart sound beat signals to processing circuitry 50 for analysis, e.g., for use in cardiac rhythm discrimination, and / or for analysis to detect T-waves for determination of a TR interval in the sensed cardiac EGM. In some examples, a TR interval may be an interval from a T-wave to a next R-wave. In some examples, a TR interval may be an interval from a T-wave peak to a peak of a next R-wave. Sensing circuitry 52 may include one or more detection channels, each of which may include an amplifier. The detection channels may be used to sense cardiac signals, such as a cardiac EGM. Some detection channels may detect events, such as R-waves, P-waves, and T-waves and provide indications of the occurrences of such events to processing circuitry 50. One or more other detection channels may provide the signals to an analog-to-digital converter, for conversion into a digital signal for processing or analysis by processing circuitry 50.

[0045] Further examples of cardiac EGM T-wave detection described herein are described in commonly assigned U.S. Patent No. 10,772,525, the entire contents of which is incorporated herein by reference.

[0046] Based at least in part on the TR interval of a sensed cardiac EGM corresponding to a heart sound beat signal, processing circuitry 50 may determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal. In some examples, processing circuitry 50 may store the digitized cardiac EGM signal and / or heart sound beat signal in storage device 56. Processing circuitry 50 of IMD 10, and / or processing circuitry of another device that retrieves data from IMD 10, may analyze the cardiac EGM signal and heart sound beat signal to determine a TR interval of the sensed cardiac EGM and determine one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal, as describe herein.

[0047] Communication circuitry 54 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 26. In addition, processing circuitry 50 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLink™ Network. Antenna 26 and communication circuitry 54 may be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0048] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. Storage device 56 may store, as examples, programmed values for one or more operational parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include HF detection quantifications / indications, digitized EGM signals, and / or digitized heart sound beat signals, as examples.

[0049] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of IMD 10 of FIGS. 1 and 2. In the example shown in FIG. 3, IMD 10 may include a leadless, subcutaneously-implantable monitoring device having a housing 15 and an insulative cover 76. Electrode 16A and electrode 16B may be formed or placed on an outer surface of cover 76. Circuitries 50-62, described above with respect to FIG. 2, may be formed or placed on an inner surface of cover 76, or within housing 15. In the illustrated example, antenna 26 is formed or placed on the inner surface of cover 76, but may be formed or placed on the outer surface in some examples. In some examples, oneor more of sensors 62 may be formed or placed on the outer surface of cover 76. In some examples, insulative cover 76 may be positioned over an open housing 15 such that housing 15 and cover 76 enclose antenna 26 and circuitries 50-62, and protect the antenna and circuitries from fluids such as body fluids.

[0050] One or more of antenna 26 or circuitries 50-62 may be formed on the inner side of insulative cover 76, such as by using flip-chip technology. Insulative cover 76 may be flipped onto a housing 15. When flipped and placed onto housing 15, the components of IMD 10 formed on the inner side of insulative cover 76 may be positioned in a gap 78 defined by housing 15. Electrodes 16 may be electrically connected to switching circuitry 58 through one or more vias (not shown) formed through insulative cover 76. Insulative cover 76 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Housing 15 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16 may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, 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.

[0051] FIG. 4A is a conceptual drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIGS. 1-3 as an ICM. In the example shown in FIG. 4A, IMD 10A may be embodied as a monitoring device having housing 15, proximal electrode 16A and distal electrode 16B. Housing 15 may further comprise first major surface 14, second major surface 18, proximal end 20, and distal end 22. Housing 15 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes 16A and 16B.

[0052] In the example shown in FIG. 4 A, IMD 10A is defined by a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during and after insertion. For example, the device shown in FIG. 4A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in theproper orientation following insertion. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30 millimeters (mm) to 55mm, 35mm to 55mm, and from 40mm to 55mm and may be any range or individual spacing from 25mm to 60mm. In addition, IMD 10A may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 15mm to 50mm, 40mm to 60mm, 45mm to 60mm and may be any length or range of lengths between about 5mm and about 80mm. In addition, the width W of major surface 14 may range from 5mm to 15mm, 3mm to 10mm, and may be any single or range of widths between 3mm and 15mm. The thickness of depth D of IMD 10A may range from 2mm to 9mm. In other examples, the depth D of IMD 10A may range from 2mm to 5mm, may range from 5mm to 15mm, and may be any single or range of depths from 2mm to 15mm. In addition, IMD 10A according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic cm.

[0053] In the example shown in FIG. 4A, once inserted within the patient, the first major surface 14 faces outward, toward the skin of the patient while the second major surface 18 is located opposite the first major surface 14. In addition, in the example shown in FIG. 4A, proximal end 20 and distal end 22 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. IMD 10A, including instrument and method for inserting IMD 10 is described, for example, in U.S. Patent Publication No. 2014 / 0276928, incorporated herein by reference in its entirety.

[0054] Proximal electrode 16A and distal electrode 16B are used to sense cardiac signals, e.g. EGM signals, intra-thoracically or extra-thoracically, which may be sub- muscularly or subcutaneously. EGM signals may be stored in a memory of IMD 10A, and data may be transmitted via integrated antenna 30A to another medical device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 16A and 16B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an ECG, EGM, EEG, EMG, or a nerve signal, from any implanted location.

[0055] In the example shown in FIG. 4A, proximal electrode 16A is in close proximity to the proximal end 20 and distal electrode 16B is in close proximity to distal end 22. Inthis example, distal electrode 16B is not limited to a flattened, outward facing surface, but may extend from first major surface 14 around rounded edges 24 and / or end surface 25 and onto the second major surface 18 so that the electrode 16B has a three-dimensional curved configuration. In some examples, electrode 16B is an uninsulated portion of a metallic, e.g., titanium, part of housing 15.

[0056] In the example shown in FIG. 4 A, proximal electrode 16A is located on first major surface 14 and is substantially flat, and outward facing. However, in other examples proximal electrode 16A may utilize the three dimensional curved configuration of distal electrode 16B, providing a three dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 16B may utilize a substantially flat, outward facing electrode located on first major surface 14 similar to that shown with respect to proximal electrode 16 A.

[0057] The various electrode configurations allow for configurations in which proximal electrode 16A and distal electrode 16B are located on both first major surface 14 and second major surface 18. In other configurations, such as that shown in FIG. 4A, only one of proximal electrode 16A and distal electrode 16B is located on both major surfaces 14 and 18, and in still other configurations both proximal electrode 16A and distal electrode 16B are located on one of the first major surface 14 or the second major surface 18 (e.g., proximal electrode 16A located on first major surface 14 while distal electrode 16B is located on second major surface 18). In another example, IMD 10A may include electrodes on both major surface 14 and 18 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A. Electrodes 16A and 16B may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.

[0058] In the example shown in FIG. 4A, proximal end 20 includes a header assembly 28 that includes one or more of proximal electrode 16A, integrated antenna 30A, antimigration projections 32, and / or suture hole 34. Integrated antenna 30A is located on the same major surface (i.e., first major surface 14) as proximal electrode 16A and is also included as part of header assembly 28. Integrated antenna 30A allows IMD 10A to transmit and / or receive data. In other examples, integrated antenna 30A may be formed on the opposite major surface as proximal electrode 16A, or may be incorporated withinthe housing 15 of IMD 10A. In the example shown in FIG. 4A, anti-migration projections 32 are located adjacent to integrated antenna 30A and protrude away from first major surface 14 to prevent longitudinal movement of the device. In the example shown in FIG. 4A, anti-migration projections 32 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 14. As discussed above, in other examples anti-migration projections 32 may be located on the opposite major surface as proximal electrode 16A and / or integrated antenna 30A. In addition, in the example shown in FIG. 4A, header assembly 28 includes suture hole 34, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 34 is located adjacent to proximal electrode 16A. In one example, header assembly 28 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10A.

[0059] FIG. 4B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIGS. 1-3. IMD 10B of FIG. 4B may be configured substantially similarly to IMD lOA of FIG. 4A, with differences between them discussed herein.

[0060] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g. an ICM. IMD 10B includes housing having a base 40 and an insulative cover 42. Proximal electrode 16C and distal electrode 16D may be formed or placed on an outer surface of cover 42. Various circuitries and components of IMD 10B, e.g., described below with respect to FIG. 3, may be formed or placed on an inner surface of cover 42, or within base 40. In some examples, a battery or other power source of IMD 10B may be included within base 40. In the illustrated example, antenna 30B is formed or placed on the outer surface of cover 42, but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 42 may be positioned over an open base 40 such that base 40 and cover 42 enclose the circuitries and other components and protect them from fluids such as body fluids.

[0061] Circuitries and components may be formed on the inner side of insulative cover 42, such as by using flip-chip technology. Insulative cover 42 may be flipped onto a base 40. When flipped and placed onto base 40, the components of IMD 10B formed on the inner side of insulative cover 42 may be positioned in a gap 44 defined by base 40. Electrodes 16C and 16D and antenna 30B may be electrically connected to circuitryformed on the inner side of insulative cover 42 through one or more vias (not shown) formed through insulative cover 42. Insulative cover 42 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Base 40 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16C and 16D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16C and 16D 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.

[0062] In the example shown in FIG. 4B, the housing of IMD 10B defines a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 4B. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30mm to 50mm, from 35mm to 45mm, or be approximately 40mm. In addition, IMD 10B may have a length E that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 40mm to 60mm, 45mm to 55mm, or be approximately 45mm. In addition, the width W may range from 3mm to 15mm, such as approximately 8mm. The thickness of depth D of IMD 10B may range from 2mm to 15mm, from 3 to 5mm, or be approximately 4mm. IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.

[0063] In the example shown in FIG. 4B, once inserted subcutaneously within the patient, outer surface of cover 42 faces outward, toward the skin of the patient. In addition, as shown in FIG. 4B, proximal end 46 and distal end 48 are rounded to reduce discomfort and irritation to surrounding tissue once inserted

[0064] FIG. 5 is a block diagram illustrating an example configuration of components of external device 12. In the example of FIG. 5, external device 12 includes processing circuitry 80, communication circuitry 82, storage device 84, and user interface 86.

[0065] Processing circuitry 80 may include one or more processors that are configured to implement functionality and / or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combinationof any of the foregoing devices or circuitry. Accordingly, processing circuitry 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 80.

[0066] Communication circuitry 82 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from, as well as send uplink telemetry to, IMD 10, or another device. Communication circuitry 82 may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of a variety of forms of wired and / or wireless communication and / or network protocols.

[0067] Storage device 84 may be configured to store information within external device 12 during operation. Storage device 84 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 84 includes one or more of a short-term memory or a long-term memory. Storage device 84 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicative of instructions for execution by processing circuitry 80. Storage device 84 may be used by software or applications running on external device 12 to temporarily store information during program execution.

[0068] Data exchanged between external device 12 and IMD 10 may include operational parameters. External device 12 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters and / or export collected data. For example, processing circuitry 80 may transmit an instruction to IMD 10 which requests IMD 10 to export collected data (e.g., HF data, heart sound features, digitized cardiac EGM signals, and / or digitized heart sound beat signals) to external device 12. In turn, external device 12 may receive the collected data from IMD 10 and store the collected data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein toanalyze heart sound features, cardiac electrogram signals, and / or heart sound beat signals received from IMD 10, e.g., to determine an indication of HF.

[0069] A user, such as a clinician or patient 4, may interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry 80 may present information related to IMD 10, e.g., cardiac EGMs, heart sound beat signals, heart sound features, indications of detections or predictions of HF, and quantifications of HF, such as a quantification of change in HF over a period of time (e.g., improving or worsening) or likelihood of an HF event. In addition, user interface 86 may include an input mechanism to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

[0070] FIG. 6 is a block diagram illustrating an example system that includes an access point 90, a network 92, external computing devices, such as a server 94, and one or more other computing devices 100A-100N (collectively, “computing devices 100”), which may be coupled to IMD 10 and external device 12 via network 92, in accordance with one or more techniques described herein. In this example, IMD 10 may use communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 90 via a second wireless connection. In the example of FIG. 6, access point 90, external device 12, server 94, and computing devices 100 are interconnected and may communicate with each other through network 92.

[0071] Access point 90 may include a device that connects to network 92 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 90 may be coupled to network 92 through different forms of connections, including wired or wireless connections. In some examples, access point 90 may be a user device, such as a tablet or smartphone, that maybe co-located with the patient. IMD 10 may be configured to transmit data, such as digitized cardiac EGMs, digitized heart sound signals, extracted heart sound features, of HF indications to access point 90. Access point 90 may then communicate the retrieved data to server 94 via network 92.

[0072] In some cases, server 94 may be configured to provide a secure storage site for data that has been collected from IMD 10 and / or external device 12. In some cases, server 94 may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 100. One or more aspects of the illustrated system of FIG. 6 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareEink™ Network. In some examples, server 94 may communicate with computing device 100 via network 92. For example, server 94 may communicate an analysis of data, such as determination of a position of an S2, S3, or S4 window or HF information determined based on heart sound features, to computing device 100, external device 12, or any other computing device via network 92. For example, server 94 may communicate that a patient has HF or communicate a quantification of the HF level of the patient to computing device 100, external device 12, or any other computing device via network 92.

[0073] In some examples, one or more of computing devices 100 may be a tablet or other smart device located with a clinician, by which the clinician may program, receive alerts from, and / or interrogate IMD 10. For example, the clinician may access data collected by IMD 10 through a computing device 100, such as when patient 4 is in in between clinician visits, to check on a status of a medical condition. In some examples, the clinician may enter instructions for a medical intervention for patient 4 into an application executed by computing device 100, such as based on a status of a patient condition determined by IMD 10, external device 12, server 94, or any combination thereof, or based on other patient data known to the clinician. Device 100 then may transmit the instructions for medical intervention to another of computing devices 100 located with patient 4 or a caregiver of patient 4. For example, such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, or to seek medical attention. In further examples, a computing device 100 may generate an alert to patient 4 based on a status of a medical condition of patient 4, which may enable patient 4 proactively to seek medicalattention prior to receiving instructions for a medical intervention. In this manner, patient 4 may be empowered to take action, as needed, to address his or her medical status, which may help improve clinical outcomes for patient 4.

[0074] In the example illustrated by FIG. 6, server 94 includes a storage device 96, e.g., to store data retrieved from IMD 10, and processing circuitry 98. Although not illustrated in FIG. 6 computing devices 100 may similarly include a storage device and processing circuitry. Processing circuitry 98 may include one or more processors that are configured to implement functionality and / or process instructions for execution within server 94. For example, processing circuitry 98 may be capable of processing instructions stored in memory 96. Processing circuitry 98 may include or be coupled to communication circuitry that may include any suitable hardware, firmware, software or any combination thereof for communicating with another device. In some examples, a description of processing circuitry 98 outputting a signal, such as a classification, may include processing circuitry 98 causing communication circuitry of server 94 to output the signal. Processing circuitry 98 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 98 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 98. Processing circuitry 98 of server 94 and / or the processing circuity of computing devices 100 may implement any of the techniques described herein to analyze cardiac EGM signals, heart sound beat signals, and / or cardiac heart sound features received from IMD 10, e.g., to determine a TR interval of the sensed cardiac EGM signal, determine a position of an S2 window, S3 window, or S4 window of the heart sound beat signal, determine / extract cardiac heart sound features, and / or determine a detection / quantification of HF.

[0075] Storage device 96 may include a computer-readable storage medium or computer-readable storage device. In some examples, memory 96 includes one or more of a short-term memory or a long-term memory. Storage device 96 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 96 is used to store data indicative of instructions for execution by processing circuitry 98.

[0076] Although the techniques for determining a TR interval of the sensed cardiac EGM signal, determining a position of an S2 window, S3 window, and / or S4 window of the heart sound beat signal, extracting / determining cardiac heart sound features, and / or determining / outputting a detection / quantification / indication of HF of the patient based on the extracted cardiac heart sound features, are described herein primarily (e.g., with respect to FIGS. 7-9) as being performed by processing circuitry 50 of IMD 10, such techniques may be performed, in whole or part, by processing circuitry of any one or more devices of system 2, such as processing circuitry 80 of external device 12, processing circuitry 98 of server 94, or processing circuitry of one or more computing devices 100.

[0077] FIG. 7 shows examples of a cardiac EGM graph 130, SI graph 132, S2 graph 134, S3 graph 136, and S4 graph 138. Cardiac EGM graph 130 includes an example of a sensed cardiac EGM signal 120, a TR interval 122 of the sensed cardiac EGM signal 120, and a heart sound beat signal 124, e.g., the heart sound signal during a single cardiac cycle, which may be demarcated by consecutive R-waves detected by processing circuitry 50 of IMD 10. SI graph 132 shows an example of an SI window 125 of the heart sound beat signal 124. S2 graph 134 shows an example of an S2 window 126 of the heart sound beat signal 124. S3 graph 136 shows an example of an S3 window 127 of the heart sound beat signal 124. S4 graph 138 shows an example of an S4 window 128 of the heart sound beat signal 124. Each of graphs 132-138 includes the portion of heart sound beat signal124 occurring during the respective window 125-128.

[0078] In some examples, processing circuitry 50 may extract heart sound(s) of the patient from sensed cardiac vibrations, such as cardiac vibrations sensed by heart sound sensor(s) 62A. In some examples, processing circuitry 50 may filter the extracted heart sound(s) using a filter, such as a bandpass filter. In some examples, processing circuitry 50 may segment that filter extracted heart sounds to heart sound beat signal(s) based on an RR interval, such as an RR interval in an EGM signal. In some examples, processing circuitry 50 may store a plurality of heart sound beat signals from the extracted heart sound(s) over a period of time, e.g., 10-30 seconds, 30-60 seconds, 5-60 seconds, or any time period in between, in a buffer. In some examples, processing circuitry 50 may apply a zero-crossing threshold to a heart sound beat signal to determine whether a respective heart sound beat signal of the plurality of heart sound beat signals to delineate between “good” heart sound beats and “noisy” heart sound beats. For example, processing circuitry50 may determine a respective heart sound beat signal satisfies the zero-crossing threshold and identify that respective heart sound beat signal as “good”. Processing circuitry 50 may determine a respective heart sound beat signal does not satisfy the zero-crossing threshold and identify that respective heart sound beat signal as “noisy” or something similar. In some examples, processing circuitry 50 may determine a number of zero-crossings in a respect heart sound beat with respect to a lower end zero crossing threshold to eliminate / reduce low frequency noise and with respect to a higher end zero crossing threshold frequency threshold to eliminate / reduce high frequency noise. In some examples, processing circuitry 50 may normalize a zero crossing threshold to a length of a respective heart sound beat. In some examples, processing circuitry 50 may discard / filter the heart sound beat signals identified as noisy and use the non-noisy heart sound beat signals. In some examples, processing circuitry 50 may periodically extract heart sound beat samples (e.g., each sample ranging between 5 and 60 seconds as discussed above), such as once every hour, once every 6 hours, once every 12 hours, once per day, or other periods of time. In some examples, processing circuitry 50 may cache information derived from the respective heart sound beat samples, such as a heart sound amplitude in each heart sound beat sample, and output the cached information and / or determine one or more aspects of the cardiac health of the patient based on the cached information.

[0079] In some examples, processing circuitry 50 may determine a template heart sound beat signal based on a first or random non-noisy heart sound beat from the buffer. In some examples, a template heart sound beat may be adaptive to particular features of the heart, such as the template heart sound beat being adaptive to changes in heart rate. In some examples, processing circuitry 50 may cross-correlate each of the plurality of heart sound beat signals to identify the heart sound beat signal with the most similarities to the other heart sound beat signals and select the identified heart sound beat signal with the most similarities to be the template heart sound beat signal. In some examples, processing circuitry 50 may align the plurality of heart sound beat signals using a template heart sound beat. For example, processing circuitry 50 may align a plurality of non-noisy heart sound beat signals based on a template heart sound beat signal using cross correlation location.

[0080] In some examples, processing circuitry 50 may determine a position of an SI window 125 of the heart sound beat signal 124. For example, processing circuitry 50 maydetermine an SI window 125 of the heart sound beat signal 124 based on an R peak 121A and / or T peak 12 IB of the sensed EGM signal 120. In some examples, an SI window may be a predetermined period of time after an R wave peak. For example, an S 1 window may be 10 milliseconds (ms) to 190 ms after R peak 121 A. In some examples, an SI window may begin 40 ms prior to an R wave marker and may end 150 ms after the R wave marker. In some examples, an S 1 window may vary based on one or more of the heart rate, RR interval, or TR interval.

[0081] Processing circuitry 50 may determine a TR interval 122 of the sensed EGM 120 corresponding to heart sound beat signal 124. In some examples, a TR interval 122 may be an interval from a T-wave to a next R-wave. In some examples, a TR interval 122 may be an interval from a T-wave peak, such as T peak 121B, to a peak of a next R-wave., such as R peak 121C. In some examples, processing circuitry 50 may determine a TR interval 122 of the sensed EGM 120 corresponding to heart sound beat signal 124 based on the T peak 12 IB and the R peak 121C of the sensed EGM signal 120. In some examples, as shown in FIG. 7, processing circuitry 50 may determine a position of one or more of an S2 window 126, S3 window 127, or S4 window of the heart sound beat signal 124 based, at least in part, on the determined TR interval 122 of the sensed EGM signal 120. In some examples, processing circuitry 50 may determine a position of one or more of an S2 window 126, S3 window 127, or S4 window of the heart sound beat signal 124 based, at least in part, on an RR interval (e.g., interval from R peak 121A to R peak 121C) of the sensed EGM signal 120. In some examples, processing circuitry 50 may determine a position of one or more of an S2 window 126, S3 window 127, or S4 window of the heart sound beat signal 124 based, at least in part, on an RR interval and a TR interval of the sensed EGM signal 120.

[0082] In some examples, an SI window may be based on an R wave location. For example, an SI window may begin 40 ms prior to an R wave marker and may end 150 ms after the R wave marker. In some examples, an SI window may begin at a position SIB that corresponds to an end of the previous S4 window, and the S 1 window ends at a position S1E that corresponds to an S2 window position, as shown as S2P, minus an nth percentage of duration of a TR interval 122 (e.g., S2P - (n% * TR interval duration 122). In some examples, the nth percentage is between 3-5%. In some examples, the nth percentage is 4%. In some examples, a position of an S2 window may be based on a Twave location. For example, if an RT interval is greater than 1.4 times a TR interval, where the RT interval indicates a systolic period and the TR interval indicates a diastolic period, an S2 window may begin at a location corresponding to a T wave location minus 2% of the TR duration and the S2 window may end at a location corresponding to a T wave location plus 70% of the TR duration. If RT interval is less than 1.4 times the TR interval, an S2 window may begin at a location corresponding to a T wave minus 2% of the TR duration and the S2 window may end at a location correspond to a T wave location plus 40% of the TR duration.

[0083] In some examples, a position of an S3 window may be based on an S2 window location. For example, an S3 window may begin at a location corresponding to the S2 window plus 22% of the TR duration and an S3 window may end at a P wave location or at a position corresponding to the S2 window plus 60% of the TR duration. In some examples, a position of an S3 window may be based on a T wave location. For example, an S3 window may begin at a T wave location plus 50% of TR duration and may end at a P wave location or a T wave location plus 70% of TR duration.

[0084] In some examples, a position of an S4 window may be based on an S2 window location. For example, an S4 window may begin at a location corresponding to the S2 window plus 55% of the TR duration or a P wave location and an S4 window may end at a length of a beat 1. In some examples, a position of an S4 window may be based on a T wave location. For example, an S4 window may begin at a T wave location plus 80% of TR duration a P wave location and may end at a length of a beat 1.

[0085] In some examples, processing circuitry 50 may set the RT wave 123A portion (e.g., from R peak 121 A to T peak 12 IB or other aspects of the R-wave and T-wave of the sensed cardiac EGM signal 120) of the heart sound beat signal 124 to zero 123B, 123C, as shown in S2 graph 134 and S3 graph 136 to align a position of one or more of the S2 window 126, S3 window 127, or S4 window 128 of respective heart sound beat signals 124 of a plurality of heart sound beat signals. An SI to S2 interval of a patient may change by a small amount from beat to beat and the amplitude of the heart sound beat signal 124 in the SI window may be larger than the amplitude of the heart sound beat signal 124 in the S2 window 126, S3 window 127, and / or S4 window 128. In some examples, setting the RT wave 123A in the SI window 125 to zero may improve the alignment of the positions of the S2 window 126, S3 window 127, and / or S4 window 128.

[0086] In some examples, processing circuitry 50 may perform ensemble averaging on the plurality of heart sound beat signals to generate heart sound beat signal 124. In this manner, heart sound beat signal 124 may be an ensemble average heart sound beat signal. In some examples, processing circuitry 50 may perform ensemble averaging on the plurality of filtered heart sound beat signals to generate heart sound beat signal 124. In some examples, processing circuitry 50 may perform ensemble averaging on the plurality of aligned heart sound beat signals to generate heart sound beat signal 124. For example, an RR interval may change from beat to beat, processing circuitry 50 may set the RT wave 123A in the SI window 125 to zero to improve alignment of the S2-S4 windows of the plurality of heart sound beat signals before performing ensemble averaging on the plurality of heart sound beat signals to generate heart sound beat signal 124, which may reduce smudging of the peaks of the ensemble average heart sound beat signal. In some examples, performing ensemble averaging on the plurality of filtered and / or aligned heart sound beat signals to generate heart sound beat signal 124 may improve the signal-to-noise ratio of the heart sound beat signal.

[0087] In some examples, processing circuitry 50 may extract one or more of the S 1 window 125, the S2 window 126, S3 window 127, or S4 window 128 of the heart sound beat signal 124. In some examples, processing circuitry 50 may extract one or more heart sound features in one or more of the S2 window 126, S3 window 127, or S4 window 128 of the heart sound beat signal 124. In some examples, such as when the heart sound beat signal 124 is an ensemble average heart sound beat signal, processing circuitry 50 may extract one or more heart sound features in one or more of the S2 window 126, S3 window 127, or S4 window 128 of the ensemble average heart sound beat signal 124. Processing circuitry 50 may output an indication of HF of the patient based on the extracted cardiac heart sound features.

[0088] In some examples, the one or more heart sound features is based on a maximum slew rate of the heart sound beat signal for one or more of the SI window 125, S2 window 126, S3 window 127, or S4 window 128. In some examples, the one or more cardiac heart sound features is based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window 126, S3 window 127, or S4 window 128. In some examples, the one or more cardiac heart sound features may include one or more of a maximum slew rate of the heart sound beat signal for one or more of the SI window 125,52 window 126, S3 window 127, or S4 window 128, a distance between maximum slew rate of the heart sound beat signal in one window of the SI window 125, S2 window 126,53 window 127, or S4 window 128 to a maximum slew rate of the heart sound beat signal of another window of the SI window 125, S2 window 126, S3 window 127, or S4 window 128, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the SI window 125, S2 window 126, S3 window 127, or S4 window 128 to a maximum slew rate of the heart sound beat signal in another window of the SI window 125, S2 window 126, S3 window 127, or S4 window 128. In some examples, processing circuitry 50 may generate one or more heart sound features based on applying signal processing techniques, such as wavelet transforms, wavelet scattering, etc., to the heart sound beat signal 124 and then extracting particular features in different time frequency windows as determined by the position of the SI window 125, S2 window 126, S3 window 127, and / or S4 window 128.

[0089] In some examples, the one or more heart sound features may include one or more of an area under a curve of the heart sound beat signal 124 in one or more of the S 1 window 125, S2 window 126, S3 window 127, or S4 window 128. In some examples, the one or more heart sound features may include one or more of a peak-to-peak distance of the heart sound beat signal 124 in one window of the SI window 125, S2 window 126, S3 window 127, or S4 window 128 to the heart sound beat signal in another window of the SI window 125, S2 window 126, S3 window 127, or S4 window 128.

[0090] Determining a TR interval of the sensed EGM corresponding to a heart sound beat signal and determining a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM may help improve the accuracy of the S2 window, S3 window, and / or S4 window of the heart sound beat signal, which helps improve the accuracy of cardiac features extracted from particular windows of the heart sound beat signal which helps improve the accuracy of determining a HF status (e.g., HF score, HF classification, HF progression, and / or HF recovery) of the patient.

[0091] FIG. 8 is a flow diagram illustrating an example technique for operation of a medical system 2 to determine heart sound features for heart failure monitoring. As indicated by FIG. 8, processing circuitry 50 may determine a TR interval of a sensed EGM signal corresponding to a heart sound beat signal (800). Processing circuitry 50 maydetermine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM (810). Processing circuitry 50 may extract one or more cardiac heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal (820). In some examples, processing circuitry 50 may extract one or more cardiac heart sound features in one or more of the S2 window, S3 window, or S4 window of an ensemble average heart sound beat signal. Processing circuitry 50 may output an indication of HF, such as HF status, of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the heart sound beat signal (830). In some examples, processing circuitry 50 may determine a HF status (e.g., HF score, HF classification, HF progression, and / or HF recovery score) of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the heart sound beat signal and output an indication of the HF, such as an indication of the HF status. In some examples, the heart sound beat signal may be an ensemble average heart sound beat signal.

[0092] In some examples, processing circuitry 50 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to determine a HF status based on extracted heart sound features and, in some examples, one or more additional parameters of patient 4. Processing circuitry 50 may be configured to execute an artificial intelligence (Al) engine that operates according to one or more models, such as machine learning models. Machine learning models may include any number of different types of machine learning models, such as neural networks, deep neural networks, convolution neural networks, recurrent neural networks, such as long short term memory networks, dense neural networks, and the like. In some examples, various feature inputs to the Al engine may be fed as direct inputs to different layers in a network and not necessarily prior to the convolution layers. Although described with respect to machine learning models, the techniques described in this disclosure are also applicable to other types of Al models, including rule-based models, finite state machines, and the like. For example, the techniques described in this disclosure are also applicable to Bayesian Belief Networks (BBN) or Bayesian machine learning models (these sometimes referred to as Bayesian Networks or Bayesian frameworks herein), Markov random fields, graphical models, Al models (e.g., Naive Bayes classifiers or deep learning models),and / or other belief networks, such as sigmoid belief networks, deep belief networks (DBNs), etc. In other examples, the disclosed technology may leverage non-Bayesian prediction or probability modeling, such as frequentist inference modeling or other statistical models.

[0093] Machine learning may generally enable a computing device to analyze input data and identify an action to be performed responsive to the input data. Each machine learning model may be trained using training data that reflects likely input data. The training data may be labeled or unlabeled (meaning that the correct action to be taken based on a sample of training data is explicitly stated or not explicitly stated, respectively).

[0094] The training of the machine learning model may be guided (in that a designer, such as a computer programmer, may direct the training to guide the machine learning model to identify the correct action in view of the input data) or unguided (in that the machine learning model is not guided by a designer to identify the correct action in view of the input data). In some instances, the machine learning model is trained through a combination of labeled and unlabeled training data, a combination of guided and unguided training, or possibly combinations thereof. Examples of machine learning include nearest neighbor, naive Bayes, decision trees, linear regression, support vector machines, neural networks, k-Means clustering, Q-leaming, temporal difference, deep adversarial networks, evolutionary algorithms or other supervised, unsupervised, semi- supervised, or reinforcement learning algorithms to train one or more models.

[0095] Processing circuitry 50 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to determine a HF status (e.g., HF score, HF classification, HF progression, and / or HF recovery score) based on extracted cardiac heart sound features. Processing circuitry 50 may train a deep learning model to represent a relationship of the extracted cardiac features to a HF status. For example, processing circuitry 50 may train the deep learning model using cardiac sound features from other patients. In some examples, processing circuitry 50 may train the deep leaning model by adjusting the weights of a hidden layer of a neural network model to balance the contribution of each input (e.g., characteristics of the cardiac sound features) according to determining a HF status. Once the deep learning model is trained, processing circuitry 50 may obtain and apply data, such as the extracted cardiac sound features, to the trained deep learning model.

[0096] Parameters of patient 4 that may be used as inputs for an HF status determination in addition to heart sound features may include heart rate metrics, heart rate variability metrics, patient activity metrics, arrhythmia metrics, pacing therapy metrics, respiration metrics, and / or metrics of congestion, perfusion, or edema, such as may be determined based on measurements of an impedance of the patient. Further examples of patient parameters and artificial intelligence techniques that may be used to determine HF status with the heart sound metrics described herein are described in commonly assigned U.S. Patent Nos. 8,255,046 and 9,713,701, U.S App. Pub. Nos. 20160361026, 20200397308, 20210093253, and 20210093220, and International App. No. PCT / IB2023 / 053802, the entire contents of each of which are incorporated herein by reference.

[0097] FIG. 9 is an example of a machine learning model 902 and / or machine learning model 1100 being trained using supervised and / or reinforcement learning techniques, such as machine learning model 1100. Machine learning model 902 may correspond to any machine learning model described herein. The machine learning model 902 may be implemented using any number of models for supervised and / or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, naive Bayes network, support vector machine, or k-nearest neighbor model, to name only a few examples. In some examples, one or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 initially trains the machine learning model 902 based on a training set of metrics and corresponding to a HF status. The training set 900 may include a set of feature vectors, where each feature in the feature vector represents a value for a particular metric. One or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 may select a training set comprising a set of training instances, each training instance comprising an association between one or more cardiac heart sound features and a HF status. A prediction or classification by the machine learning model 902 may be compared 904 to the target output 903, and an error signal and / or machine learning model weights modification may sent / applied to the machine learning model 902 based on the comparison to leam / train 905 the machine learning model to modify / update the machine learning model 902. For example, one or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 may, for each training instance in the training set, modify, based on the respective cardiac heart sound features, and / or HF status of thetraining instance, the machine learning model 902 to change a score generated by the machine learning model 902 in response to subsequent cardiac heart sound features applied to the machine learning model 902.

[0098] FIG. 10 is a conceptual diagram illustrating an example machine learning model 1100 configured to generate one or more values indicative of a risk of a health event, e.g., for heart failure or another patient condition, based on physiological parameter values, e.g., sensed by an IMD and / or other devices as described herein. Machine learning model 1100 is an example of a deep learning model, or deep learning algorithm. One or more of IMD 10, external device 12, or sever 94 may train, store, and / or utilize machine learning model 1100, but other devices may apply inputs associated with a particular patient to machine learning model 1100 in other examples. Some non-limiting examples of machine learning techniques include Bayesian probability models, Support Vector Machines, K-Nearest Neighbor algorithms, and Multi-layer Perceptron.

[0099] As shown in the example of FIG. 10, machine learning model 1100 may include three layers. These three layers include input layer 1102, hidden layer 1104, and output layer 1106. Output layer 1106 comprises the output from the transfer function 1105 of output layer 1106. Input layer 1102 represents each of the input values XI through X4 provided to machine learning model 1100. The number of inputs may be less than or greater than 4, including much greater than 4, e.g., hundreds or thousands. In some examples, the input values may be any of the of physiological or other patient parameter values described herein.

[0100] Each of the input values for each node in the input layer 1102 is provided to each node of hidden layer 1104. In the example of FIG. 10, hidden layers 1104 include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples. Each input from input layer 1102 is multiplied by a weight and then summed at each node of hidden layers 1104. During training of machine learning model 1100, the weights for each input are adjusted to establish the relationship between input physiological parameter values and one or more output values indicative of a risk level of a health event for the patient. In some examples, one hidden layer may be incorporated into machine learning model 1100, or three or more hidden layers may be incorporated into machine learning model 1100, where each layer includes the same or different number of nodes.

[0101] The result of each node within hidden layers 1104 is applied to the transfer function of output layer 1106. The transfer function may be liner or non-linear, depending on the number of layers within machine learning model 1100. Example non-linear transfer functions may be a sigmoid function or a rectifier function. The output 1107 of the transfer function may be a value or values indicative of a risk of an HF event or other health event of the patient. By applying the patient parameter data to a machine learning model, such as machine learning model 1100, processing circuitry of system 2 is able to determine the health condition with great accuracy, specificity, and sensitivity.

[0102] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, stimulators, or other devices. The terms “processor” and “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

[0103] For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions on a computer-readable storage medium such as RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

[0104] In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, anintegrated circuit (IC) or a set of ICs, and / or discrete electrical circuitry, residing in an IMD and / or external programmer.

[0105] Various aspects of the techniques may enable the following examples.

[0106] Example 1: A system includes an insertable cardiac monitor (ICM) includes a housing configured for subcutaneous implantation within a patient; and a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a remote computing device; and processing circuitry of at least one of the ICM or the remote computing device, the processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0107] Example 2: The system of example 1, wherein the ICM further comprises a heart sound sensor within the housing, the heart sound sensor configured to sense cardiac vibrations of the patient, and generate the heart sound beat signal based on the cardiac vibrations.

[0108] Example 3: The system of example 2, wherein the heart sound sensor comprises an accelerometer.

[0109] Example 4: The system of any of examples 1-3, wherein the heart sound beat signal comprises an ensemble average of a plurality of heart sound beat signals output by the heart sound sensor over a period of time.

[0110] Example 5 : The system of any of examples 1-4, wherein the processing circuitry is configured to extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

[0111] Example 6 : The system of example 5, wherein the processing circuitry is configured to output an indication of the one or more heart sound features.

[0112] Example 7 : The system of any of examples 5-6, wherein the processing circuitry is configured to output an indication of heart failure of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

[0113] Example 8: The system of any of examples 5-7, wherein the one or more cardiac heart sound features are based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window, S3 window, or S4 window.

[0114] Example 9 : The system of any of examples 5-8, wherein the one or more heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

[0115] Example 10: The system of any of examples 5-9, wherein the one or more heart sound features include one or more of an area under a curve of the heart sound beat signal in one or more of the plurality of windows.

[0116] Example 11: The system of any of examples 4 through 10, wherein the processing circuitry is configured to align the plurality of heart sound beat signals using the sensed cardiac EGM, and wherein the heart sound beat signal comprises an ensemble average of the aligned plurality of heart sound beat signals.

[0117] Example 12: The system of any of examples 1-11, wherein the processing circuitry is configured to determine a position of an S 1 window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

[0118] Example 13: The system of example 12, wherein the processing circuitry is configured to extract one or more heart sound features in the S 1 window of the heart sound beat signal.

[0119] Example 14: The system of any of examples 1-13, the ICM further includes a power source operatively coupled to the processing circuitry; and a memory operatively coupled to the processing circuitry, wherein the plurality of electrodes are operatively coupled to the processing circuitry, wherein the housing is a hermetically- sealed housing, wherein at least the power source, memory, and processing circuitry are within the hermetically- sealed housing, and wherein the housing has a length, a width, and a depth,wherein the length is greater than the width and the width is greater than the depth, wherein the length is within a range from 5 millimeters (mm) to 60 mm, wherein the width is within a range from 5 mm to 15 mm, and wherein the depth is within a range from 5 mm to 15 mm.

[0120] Example 15: A computing device includes a memory; and processing circuitry coupled to the memory, the processing circuitry being configured to: receive a sensed cardiac electrogram (EGM)) and a sensed heart sound beat signal of a patient; determine a TR interval of the sensed EGM corresponding to the heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0121] Example 16: The computing device of example 15, wherein the sensed cardiac heart sound beat signal is based on sensed cardiac vibrations of the patient.

[0122] Example 17: The computing device of example 16, wherein the sensed cardiac vibrations are sensed by an accelerometer.

[0123] Example 18: The computing device of any of examples 15-17, wherein the heart sound beat signal comprises an ensemble average of a plurality of heart sound beat signals output by the heart sound sensor over a period of time.

[0124] Example 19: The computing device of any of examples 15-18, wherein the processing circuitry is further configured to: extract one or more cardiac heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

[0125] Example 20: The computing device of example 19, wherein the processing circuitry is further configured to output an indication of the one or more heart sound features.

[0126] Example 21: The computing device of any of examples 19-20, wherein the processing circuitry is configured to output an indication of heart failure of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

[0127] Example 22: The computing device of any of examples 19-21, wherein the one or more cardiac heart sound features are based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window, S3 window, or S4 window.

[0128] Example 23: The computing device of any of examples 19-22, wherein the one or more cardiac heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

[0129] Example 24: The computing device of any of examples 19-22, wherein the one or more cardiac heart sound features include one or more of an area under a curve of the heart sound beat signal in one or more of the plurality of windows.

[0130] Example 25: The computing device of any of examples 18 through 24, wherein the processing circuitry is configured to align the plurality of heart sound beat signals using the sensed cardiac EGM, and wherein the heart sound beat signal comprises an ensemble average of the aligned plurality of heart sound beat signals.

[0131] Example 26: The computing device of any of examples 15-25, wherein the processing circuitry is configured to determine a position of an SI window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

[0132] Example 27: The computing device of example 26, wherein the processing circuitry is configured to extract one or more heart sound features in the S 1 window of the heart sound beat signal.

[0133] Example 28: An insertable cardiac monitor (ICM) includes a housing configured for subcutaneous implantation within a patient; a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a heart sound sensor within the housing, wherein the ICM is configured to sense a heart sound beat signal via the heart sound sensor; and processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to the heart sound beat signal; and determine a position of one or more ofan S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0134] Example 29: The ICM of example 28, wherein the heart sound sensor configured to sense cardiac vibrations of the patient, and generate the heart sound beat signal based on the cardiac vibrations.

[0135] Example 30: The ICM of example 29, wherein the heart sound sensor comprises an accelerometer.

[0136] Example 31: The ICM of any of examples 28-30, wherein the heart sound beat signal comprises an ensemble average of a plurality of heart sound beat signals output by the heart sound sensor over a period of time.

[0137] Example 32: The ICM of any of examples 28-31, wherein the processing circuitry is configured to extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

[0138] Example 33: The ICM of example 32, wherein the processing circuitry is configured to output an indication of the one or more heart sound features.

[0139] Example 34: The ICM of any of examples 32-33, wherein the processing circuitry is configured to output an indication of heart failure of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

[0140] Example 35: The ICM of any of examples 32-34, wherein the one or more cardiac heart sound features are based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window, S3 window, or S4 window.

[0141] Example 36: The ICM of any of examples 32-35, wherein the one or more heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

[0142] Example 37: The ICM of any of examples 32-36, wherein the one or more heart sound features include one or more of an area under a curve of the heart sound beat signal in one or more of the plurality of windows.

[0143] Example 38: The ICM of any of examples 31 through 37, wherein the processing circuitry is configured to align the plurality of heart sound beat signals using the sensed cardiac EGM, and wherein the heart sound beat signal comprises an ensemble average of the aligned plurality of heart sound beat signals.

[0144] Example 39: The ICM of any of examples 28-38, wherein the processing circuitry is configured to determine a position of an S 1 window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

[0145] Example 40: The ICM of example 39, wherein the processing circuitry is configured to extract one or more heart sound features in the S 1 window of the heart sound beat signal.

[0146] Example 41: The ICM of any of examples 28-40, wherein the ICM further comprises: a power source operatively coupled to the processing circuitry; and a memory operatively coupled to the processing circuitry, wherein the plurality of electrodes are operatively coupled to the processing circuitry, wherein the housing is a hermetically- sealed housing, wherein at least the power source, memory, and processing circuitry are within the hermetically-sealed housing, and wherein the housing has a length, a width, and a depth, wherein the length is greater than the width and the width is greater than the depth, wherein the length is within a range from 5 millimeters (mm) to 60 mm, wherein the width is within a range from 5 mm to 15 mm, and wherein the depth is within a range from 5 mm to 15 mm.

[0147] Example 42: A method includes receiving a sensed cardiac electrogram (EGM)) and a sensed heart sound beat signal of a patient; determining a TR interval of the sensed EGM corresponding to the heart sound beat signal; and determining a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

[0148] Example 43: The method of example 42, wherein the sensed cardiac heart sound beat signal is based on sensed cardiac vibrations of the patient.

[0149] Example 44: The method of example 43, wherein the sensed cardiac vibrations are sensed by an accelerometer.

[0150] Example 45: The method of any of examples 42-44, wherein the heart sound beat signal comprises an ensemble average of a plurality of heart sound beat signals output by a heart sound sensor over a period of time.

[0151] Example 46: The method of any of examples 42-45, wherein the method further comprises: extracting one or more cardiac heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

[0152] Example 47: The method of example 46, wherein the method further comprises: outputting an indication of the one or more heart sound features.

[0153] Example 48: The method of any of examples 46-47, wherein the method further comprises: outputting an indication of heart failure of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

[0154] Example 49: The method of any of examples 46-48, wherein the one or more cardiac heart sound features are based on a maximum slew rate of the heart sound beat signal for one or more of the S2 window, S3 window, or S4 window.

[0155] Example 50: The method of any of examples 46-49, wherein the one or more cardiac heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

[0156] Example 51: The method of any of examples 46-50, wherein the one or more cardiac heart sound features include one or more of an area under a curve of the heart sound beat signal in one or more of the plurality of windows.

[0157] Example 52: The method of any of examples 45 through 51, wherein the method further comprises: aligning the plurality of heart sound beat signals using the sensed cardiac EGM, and wherein the heart sound beat signal comprises an ensemble average of the aligned plurality of heart sound beat signals.

[0158] Example 53: The method of any of examples 45-52, wherein the method further comprises: determining a position of an SI window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window, and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

[0159] Example 54: The method of example 53, wherein the method further comprises extracting one or more heart sound features in the S 1 window of the heart sound beat signal.

[0160] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. A system comprising: an insertable cardiac monitor (ICM) comprising: a housing configured for subcutaneous implantation within a patient; and a plurality of electrodes on the housing, wherein the ICM is configured to sense a cardiac electrogram (EGM) of the patient via the plurality of electrodes; a remote computing device; and processing circuitry of at least one of the ICM or the remote computing device, the processing circuitry configured to: determine a TR interval of the sensed cardiac EGM corresponding to a heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

2. The system of claim 1, wherein the ICM further comprises a heart sound sensor within the housing, the heart sound sensor configured to sense cardiac vibrations of the patient, and generate the heart sound beat signal based on the cardiac vibrations.

3. The system of any of claims 1-2, wherein the heart sound beat signal comprises an ensemble average of a plurality of heart sound beat signals output by the heart sound sensor over a period of time.

4. The system of any of claims 1-3, wherein the processing circuitry is configured to extract one or more heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

5. The system of claim 4, wherein the processing circuitry is configured to output an indication of heart failure of the patient based on the extracted cardiac heart sound featuresin the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

6. The system of any of claims 4-5, wherein the one or more heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

7. The system of claim 3, wherein the processing circuitry is configured to align the plurality of heart sound beat signals using the sensed cardiac EGM, and wherein the heart sound beat signal comprises an ensemble average of the aligned plurality of heart sound beat signals.

8. The system of any of claims 1-7, wherein the processing circuitry is configured to determine a position of an S 1 window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

9. The system of claim 8, wherein the processing circuitry is configured to extract one or more heart sound features in the S 1 window of the heart sound beat signal.

10. A computing device comprising: a memory; and processing circuitry coupled to the memory, the processing circuitry being configured to: receive a sensed cardiac electrogram (EGM)) and a sensed heart sound beat signal of a patient; determine a TR interval of the sensed EGM corresponding to the heart sound beat signal; and determine a position of one or more of an S2 window, S3 window, or S4 window of the heart sound beat signal based, at least in part, on the determined TR interval of the sensed EGM.

11. The computing device of claim 10, wherein the processing circuitry is further configured to: extract one or more cardiac heart sound features in one or more of the S2 window, S3 window, or S4 window of the heart sound beat signal.

12. The computing device of claim 11, wherein the processing circuitry is configured to output an indication of heart failure of the patient based on the extracted cardiac heart sound features in the one or more of the S2, S3, or S4 window of the ensemble average heart sound beat signal.

13. The computing device of any of claims 10-12, wherein the one or more cardiac heart sound features include one or more of a maximum slew rate of the heart sound beat signal for one or more of the plurality of windows, a distance between maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal of another window of the plurality of windows, or a ratio of a maximum slew rate of the heart sound beat signal in one window of the plurality of windows to a maximum slew rate of the heart sound beat signal in another window of the plurality of windows.

14. The computing device of any of claims 10-13, wherein the processing circuitry is configured to determine a position of an S 1 window of the heart sound beat signal based, at least in part, on an end position of an S4 window of a prior heart sound beat signal, the position of the S2 window and a duration of the TR interval, wherein the prior heart sound beat signal immediately precedes the heart sound beat signal.

15. The computing device of claim 14, wherein the processing circuitry is configured to extract one or more heart sound features in the S 1 window of the heart sound beat signal.

Images