Method and system for monitoring cardiac function based on cardiac sound centroid

Abstract

A system and method for monitoring cardiac function based on heart sound (HS) are provided. The system includes electrodes configured to sense an electrocardiogram (ECG) activity (CA) signal over a period of time; an HS sensor configured to sense an HS signal over a period of time. The system includes a memory for storing specific executable instructions and includes one or more processors that, when executing the specific executable instructions, are configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signal. The processor overlays an HS search window onto an HS segment of the HS signal based on the COI from the CA signal and calculates a center of mass (COM) of at least one of S1HS or S2HS based on the HS segment of the HS signal within the search window to obtain at least one of the corresponding S1COM or S2COM. The processor calculates at least one of an electro-mechanical activation time (EMAT) or a systolic interval (SI) based on at least one of S1COM or S2COM and records at least one of EMAT or SI.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Application Serial No. 63 / 188,241, filed on May 13, 2021, the entire subject matter of which is incorporated herein by reference in its entirety.

[0003] This application is related to and filed on the same day as co-pending U.S. Application Serial No. 17 / 667,172 (Docket 14580USO2) (013-0429US2), the entire subject matter of which is incorporated herein by reference in its entirety. Technical Field

[0004] Embodiments of the present disclosure generally relate to methods and systems for monitoring cardiac function based on heart sounds. Background Art

[0005] Implantable medical devices (IMDs) are now widely used in various applications to monitor and treat various physiological conditions. Recently, there has been an interest in using heart sounds as cardiac biomarkers, such as in providing clinically useful information related to ventricular systole in various valve-related diseases.

[0006] Microelectromechanical systems (MEMS) technology-based micro accelerometers have been proposed to detect heart sounds when the accelerometer is implanted in an IMD. Traditional heart sound monitoring techniques typically monitor aspects such as heart sound duration, heart sound amplitude, the interval between heart sound peaks, the interval between the R-wave peak and the heart sound peak, etc.

[0007] However, traditional methods of using heart sounds may encounter certain limitations. For example, various factors may affect the quality of the heart sound signal, such as the position and / or orientation of the IMD. When the quality of the heart sound signal is low, it is difficult to identify heart sound features of interest, such as heart sound duration or peaks. When peaks are detected incorrectly, inaccuracies may lead to incorrect determination of corresponding intervals, such as the interval between S1 and S2 peaks, the interval between the R-wave peak and the S1 peak, etc. Incorrect determination of the intervals of interest may lead to incorrect determination of cardiac function.

[0008] In addition, some traditional methods may analyze the "area under the curve" within the S1 and / or S2 heart sounds. However, the AOC calculation of the S1 and S2 heart sounds does not provide a specific time point within the heart sound for measuring the interval, such as the time point between a point of interest in the QRS complex and a point in the S1 or S2 heart sound. In addition, the AOC does not support the determination of the systolic interval because the AOC does not specify a specific point in each of the S1 and S2 heart sounds.

[0009] There is still a need to improve the monitoring of cardiac function based on heart sounds. Summary of the Invention

[0010] According to an embodiment of the present disclosure, a system for monitoring cardiac function based on heart sound (HS) is provided. The system includes electrodes configured to sense an electrocardiogram (ECG) activity (CA) signal over a period of time; and an HS sensor configured to sense an HS signal over a period of time. The system includes a memory for storing specific executable instructions and includes one or more processors that, when executing the specific executable instructions, are configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signal. The processor overlays an HS search window onto an HS segment of the HS signal based on the COI from the CA signal and calculates a center of mass (COM) of at least one of S1 or S2 HS based on the HS segment of the HS signal within the search window to obtain at least one of a corresponding S1 COM or S2 COM. The processor calculates at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on at least one of S1 COM or S2 COM and records at least one of EMAT or SI.

[0011] Optionally, the HS search window includes S1 and S2 search windows. The one or more processors may be configured to overlay the S1 and S2 search windows onto the corresponding HS segment. The one or more processors may be configured to align the S1 search window on the HS signal to start at or near the R wave peak. The R wave peak may represent the COI. The one or more processors may be configured to align the S2 search window on the HS signal to start at a predetermined interval after one of the end of the S1 search window or the R wave peak. The R wave peak may represent the COI. S1 COM and S2 COM may represent corresponding time points along the CA and HS signals.

[0012] Optionally, the COI may occur at a COI time point along the CA signal. The one or more processors may be configured to calculate the EMAT by subtracting the S1 COM from the COI time point. The one or more processors may be configured to calculate the SI as the difference between the S1 COM and the S2 COM. The system may include an implantable medical device (IMD). The memory and the one or more processors may include an IMD memory and an IMD processor, respectively. The IMD processor may be configured to perform at least one of the identification, overlay, or calculation operations.

[0013] Optionally, the system may include an external device (ED) configured to communicate wirelessly with the IMD. The memory and one or more processors may include an ED memory and an ED processor, respectively. The ED processor may be configured to perform at least one of identification, overlay, and calculation operations. The ED may receive the CA and HS signals wirelessly. The ED processor may be configured to perform identification, overlay, and calculation operations. The HS sensor may include an accelerometer configured to collect multi-dimensional (MD) accelerometer data along at least two axes. The HS signal may correspond to the accelerometer data.

[0014] According to an embodiment herein, a computer-implemented method for monitoring cardiac function based on heart sound (HS) is provided. The method obtains an electrocardiogram (ECG) activity (CA) signal sensed at an implantable electrode over a period of time and obtains an HS signal sensed by an implantable HS sensor over a period of time. The method is controlled by one or more processors. The method identifies a characteristic of interest (COI) of a heartbeat from the CA signal and overlays an HS search window onto an HS segment of the HS signal based on the COI from the CA signal. The method calculates a centroid (COM) of at least one of S1 or S2 HS based on the HS segment of the HS signal within the search window to obtain at least one of the corresponding S1 COM or S2 COM, and calculates at least one of an electro-mechanical activation time (EMAT) or a systolic interval (SI) based on at least one of S1 COM or S2 COM. The method records at least one of EMAT or SI.

[0015] Optionally, the HS search window may include S1 and S2 search windows. One or more processors may be configured to overlay the S1 and S2 search windows onto the corresponding HS segments. The alignment operation may include aligning the S1 search window on the HS signal to start at or near the R-wave peak. The R-wave peak may represent the COI. The alignment operation may further include aligning the S2 search window on the HS signal to start at a predetermined interval after one of the end of the S1 search window or the R-wave peak. The R-wave peak may represent the COI.

[0016] Optionally, calculating the S1 COM may include calculating the product of i) the amplitude of the HS signal at a point along the S1 search window and ii) the position of the corresponding point along the S1 search window; summing the products to form a first sum; summing the amplitudes of the HS signal at these points to form a second sum; and dividing the first sum by the second sum.

[0017] Optionally, the COI may occur at a COI time point along the CA signal. This method may calculate the EMAT by subtracting the S1 COM from the COI time point. This method may store the EMAT and SI over a period of time and monitor the EMAT trend and SI trend over a period of time to indicate a change in a physiological or non - physiological condition. This method may wirelessly transmit the CA and HS signals from an implantable medical device (IMD) to an external device (ED). The ED may perform at least one of identification, overlay, calculation, and recording operations. The identification, overlay, or calculation operations may be implemented by the implantable medical device.

[0018] According to an embodiment herein, a leadless IMD is provided that includes: a housing; a fixation element coupled to the housing and configured to fix the IMD in or near a local chamber of the heart; an electrode provided on the housing and configured to sense an electrocardiogram (ECG) activity (CA) signal over a period of time; an HS sensor configured to sense an HS signal over a period of time; a memory storing specific executable instructions; and one or more processors configured, when executing the specific executable instructions, to: identify a feature of interest (COI) of a heartbeat from the CA signal; calculate a centroid of mass (COM) of at least one HS based on the HS signal to obtain a corresponding at least one HS COM; and calculate at least one of a therapy - related (TR) delay or a sensing - related (SR) blanking interval (BI) based on the at least one HS COM.

[0019] Optionally, the identification and calculation operations are performed in a calibration mode. The calculation operations include: calculating S1COM and S2 COM; calculating a diastolic interval (DI) based on S1 COM and S2 COM; and calculating an AV delay by subtracting an incremental value from the DI. Optionally, when in a therapy mode, the one or more processors are further configured to collect and analyze the HS signal to identify an HS of interest on a beat - by - beat basis. Optionally, when in a therapy mode, the one or more processors are further configured to manage the delivery of therapy based on the HS of interest and at least one of the TR delay or SR BI. Optionally, the one or more processors are further configured to initiate one or more event timers corresponding to at least one of the TR delay or SR BI in response to identifying the HS of interest. Optionally, the IMD is configured to be implanted in or near a ventricle, at least one TR delay includes an AV delay calculated by subtracting an incremental value from a diastolic interval defined as the interval between S1 COM and S2 COM, and the one or more processors are further configured to: identify an S2 HS; initiate an AV timer corresponding to the AV delay in response to identifying the S2 HS; and deliver ventricular therapy when an intrinsic ventricular event is not detected before the AV timer times out.

[0020] Optionally, at least one of a TR delay or an SR BI is calculated by combining an incremental value and at least one of a systolic interval, a diastolic interval, an S1-S1 interval, an S2-S2 interval, an S3-S3 interval, an S4-S4 interval, an S1-R wave interval, an S2-R wave interval, an S3-R wave interval, or an S4-R wave interval. Optionally, the IMD further includes one or more processors configured to acquire heart rate (HR) data, store the HR data with at least one of a TR delay or an SR BI to associate a first HR with at least one of a first TR delay or a first SR BI, and associate a second HR with at least one of a second TR delay or a second SR BI.

[0021] According to an embodiment herein, a computer-implemented method for monitoring cardiac function based on heart sounds (HS) in a leadless implantable medical device (IMD) includes: obtaining an electrocardiographic activity (CA) signal sensed at implantable electrodes provided on the leadless IMD over a period of time; obtaining an HS signal sensed by an implantable HS sensor over a period of time; identifying a characteristic of interest (COI) of a heartbeat from the CA signal under the control of one or more processors; calculating a centroid of mass (COM) of at least one HS based on the HS signal to obtain a corresponding at least one HS COM; and calculating at least one of a therapy-related (TR) delay or a sensed-related (SR) blanking interval (BI) based on the at least one HS COM.

[0022] Optionally, the recognition and calculation operations are performed in a calibration mode, and wherein, the calculation operations include: calculating S1COM and S2 COM; calculating the diastolic interval (DI) based on S1 COM and S2 COM; and calculating the AV delay by subtracting an incremental value from the DI. Optionally, when in the therapy mode, the method further includes collecting and analyzing the HS signal to identify the HS of interest on a beat-by-beat basis. Optionally, when in the therapy mode, the method further includes managing the delivery of therapy based on at least one of the HS of interest and the TR delay or SR BI. Optionally, the method further includes, in response to identifying the HS of interest, starting one or more event timers corresponding to at least one of the TR delay or SR BI. Optionally, the IMD is configured to be implanted in or near the ventricle, at least one of the TR delays includes an AV delay calculated by subtracting an incremental value from the diastolic interval defined as the interval between S1 COM and S2 COM, and the method further includes: identifying S2 HS; in response to identifying S2 HS, starting an AV timer corresponding to the AV delay; and delivering ventricular therapy when an intrinsic ventricular event is not detected before the AV timer expires. Optionally, at least one of the TR delay or SRBI is calculated by combining the incremental value and at least one of the systolic interval, diastolic interval, S1-S1 interval, S2-S2 interval, S3-S3 interval, S4-S4 interval, S1-R wave interval, S2-R wave interval, S3-R wave interval, or S4-R wave interval.

[0023] According to an embodiment herein, a leadless implantable medical device (IMD) is provided, which includes: a housing; a fixation element, coupled to the housing and configured to fix the IMD in or near a local chamber of the heart; an electrode, provided on the housing and configured to sense an electrocardiogram (ECG) activity (CA) signal over a period of time; an HS sensor, configured to sense an HS signal over a period of time; a memory, for storing specific executable instructions and storing at least one of a therapy-related (TR) delay or a sensing-related (SR) blanking interval (BI), at least one of the TR delay or SR BI being based on at least one HS centroid (COM) determined based on the HS signal; and one or more processors, when executing the specific executable instructions, in the therapy mode, being configured to: collect and analyze the HS signal to identify the HS of interest on a beat-by-beat basis; and manage the delivery of therapy based on at least one of the HS of interest and the TR delay or SRBI.

[0024] Optionally, one or more processors are further configured to initiate one or more event timers corresponding to at least one of a TR delay or an SR BI in response to identifying an HS of interest. Optionally, the IMD is configured to be implanted in or near a ventricle, at least one TR delay includes an AV delay calculated by subtracting an incremental value from a diastolic interval defined as an interval between S1 COM and S2 COM, and one or more processors are further configured to: identify an S2 HS; initiate an AV timer corresponding to the AV delay in response to identifying the S2 HS; and perform ventricular therapy when an intrinsic ventricular event is not detected before the AV timer times out. Optionally, the IMD is configured to be implanted in or near a ventricle, at least one TR delay includes at least one of an HS-HS interval or an HS-R wave interval calculated by combining an incremental value with at least one of a corresponding HS-HS interval or an HS-R wave interval, and one or more processors are further configured to: identify an HS of interest; initiate a timer corresponding to at least one of the HS-HS interval or the HS-R wave interval in response to identifying the HS of interest; and perform ventricular therapy when an intrinsic ventricular event is not detected before the timer times out. Optionally, the IMD further includes a sensor configured to acquire heart rate (HR) data, and one or more processors configured to adjust at least one of the TR delay or the SR BI based on the HR data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1A An example of simultaneously recorded CA signals and corresponding HS signals to be processed in accordance with embodiments herein is shown.

[0026] Figure 1B An example of simultaneously recorded CA signals and corresponding HS signals to be processed in accordance with embodiments herein is shown.

[0027] Figure 1C An example of simultaneously recorded CA signals and corresponding HS signals to be processed in accordance with embodiments herein is shown.

[0028] Figure 2 A method for monitoring cardiac function based on heart sounds in accordance with embodiments herein is shown.

[0029] Figure 3 A graphical example applied to the analysis of exemplary CA and HS signals is shown.

[0030] Figure 4 A format example in which EMAT and SI trend data can be presented is shown.

[0031] Figure 5 A method for monitoring cardiac function based on heart sounds in accordance with embodiments herein is shown.

[0032] Figure 6 Shows the operation of combining Figure 5 applied to the analysis of exemplary CA and HS signals.

[0033] Figure 7 Shows the Figure 5 example of EMAT and SI trend data generated by the process of

[0034] Figure 8 Shows an example application that can be implemented according to the embodiments herein.

[0035] Figure 9 Shows the operation of combining Figure 2 and Figure 5 applied to the analysis of exemplary CA and HS signals.

[0036] Figure 10A Shows, according to the Figure 2 process, the EMAT and SI trend data collected from the S1 and S2 COM determined from the y-axis broadband HS signal. Figure 9 of

[0037] Figure 10B Shows, according to the Figure 5 process, the EMAT and SI trend data collected from the S1 and S2 COM determined from the y-axis broadband HS signal. Figure 9 of

[0038] Figure 11 Shows an implantable medical device for subcutaneous implantation near the heart.

[0039] Figure 12 Shows an example block diagram of the IMD 1100 formed according to the embodiments herein.

[0040] Figure 13 Shows a schematic diagram of a physiological sensor implemented as an accelerometer according to the embodiments herein.

[0041] Figure 14 Provides a cross-sectional view of a patient's heart and shows a leadless implantable medical device according to the embodiments herein.

[0042] Figure 15 Shows a side view of an IMD according to an embodiment.

[0043] Figure 16 Shows a method for monitoring heart function based on heart sounds according to the embodiments herein to define one or more treatment-related delays and / or one or more sensing-related BIs.

[0044] Figure 17 Shows at Figure 16Graphical example of applying analysis during operation.

[0045] Figure 18 Illustrates the process of managing therapy and / or sensing by a leadless IMD based on HF metrics obtained according to embodiments herein.

[0046] Figure 19 Illustrates an alternative embodiment where the leadless IMD determines the TR delay and / or SR BI in combination with a rate adaptation mode, e.g., considering patient activity. Detailed Description

[0047] It will be readily understood that, in addition to the example embodiments described, the components of the embodiments generally described and illustrated in the figures herein can be arranged and designed in a variety of different configurations. Thus, as shown in the figures, the following more detailed description of the example embodiments is not intended to limit the scope of the claimed embodiments, but is merely representative of the example embodiments.

[0048] In this specification, references to "an embodiment" or "embodiments" (etc.) mean that the particular features, structures, or characteristics described in connection with the embodiment are included in at least one embodiment. Thus, the phrases "in an embodiment" or "in an embodiment" etc. appearing throughout this specification do not necessarily all refer to the same embodiment.

[0049] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments. However, those skilled in the relevant art will recognize that various embodiments can be practiced without one or more of the specific details, or using other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring the description. The following description is only by way of example and simply illustrates certain example embodiments.

[0050] The methods described herein may adopt the structures or aspects of the various embodiments (e.g., systems and / or methods) discussed herein. In various embodiments, certain operations may be omitted or added, certain operations may be combined, certain operations may be performed simultaneously, certain operations may be split into multiple operations, certain operations may be performed in a different order, or certain operations or a series of operations may be re-executed iteratively. It should be noted that according to the embodiments herein, other methods may be used. In addition, it is pointed out that these methods may be implemented in whole or in part by one or more processors of one or more devices or systems. Although the operations of some methods may be described as being performed by one or more processors of one device, additionally, some or all of such operations may be performed by one or more processors of another device described herein. For example, an IMD includes an IMD memory and one or more IMD processors, while each external device / system (ED) (e.g., anywhere within a local, remote, or healthcare system) includes an ED memory and one or more ED processors.

[0051] Terminology

[0052] The terms "aggregation" and "composition" are used interchangeably to refer to the mathematical combination (e.g., average, sum, mean, median, normalization, etc.) of two or more data values, signals, etc.

[0053] The terms "posture" and "patient posture" refer to the postural state and / or activity level of a patient, including supine, right lateral decubitus, left lateral decubitus, sitting, standing, isometric arm exercises (e.g., pushing, pulling, etc.), airbag, chest impact, device pressure (e.g., top, middle, and bottom), arm flapping, handshaking, etc.

[0054] The term "activity level" refers to the intensity and / or type of activity that a patient is currently experiencing at a given point in time, including stationary state, rest state, exercise state, walking state, etc.

[0055] The terms "cardiac activity signal", "cardiac activity signal", "CA signal", and "CA signal" (collectively referred to as "CA signal") are used interchangeably throughout to refer to an analog or digital electrical signal recorded by two or more electrodes located subcutaneously or on the skin, where the electrical signal indicates cardiac electrical activity. Cardiac activity may be normal / healthy or abnormal / arrhythmic. Non-limiting examples of CA signals include ECG signals collected through skin electrodes, and EGM signals collected through subcutaneous electrodes and / or through electrodes located on or near the heart wall and / or ventricle.

[0056] The terms "healthcare system" and "digital healthcare system" are used interchangeably throughout to refer to a system that includes devices for measuring health parameters and a communication path from the devices to auxiliary devices. The auxiliary devices may be located at the same location as the devices or at a different location remote from the devices. The communication path can be wired, wireless, over the air, cellular, in the cloud, etc. In one example, the provided healthcare system may be one of the systems described in U.S. Provisional Patent Application No. 62 / 875,870, filed on July 18, 2019, by Rupinder, titled "METHODS DEVICE AND SYSTEMS FOR HOLISTIC INTEGRATED HEALTHCARE PATIENTMANAGEMENT", the entire content of which is incorporated herein by reference. Other patents describing example monitoring systems include U.S. Patent No. 6,572,557, filed on December 21, 2000, by Tchou et al., titled "SYSTEM AND METHOD FOR MONITORING PROGRESSION OF CARDIAC DISEASE STATE USINGPHYSIOLOGIC SENSORS"; U.S. Patent No. 6,480,733, filed on December 17, 1999, by Turcott, titled "METHOD FORMONITORING HEART FAILURE"; U.S. Patent No. 7,272,443, filed on December 14, 2004, by Min et al., titled "SYSTEM AND METHOD FOR PREDICTING A HEART CONDITION BASEDON IMPEDANCE VALUES USING AN IMPLANTABLE MEDICAL DEVICE"; U.S. Patent No. 7,308,309, filed on January 11, 2005, by Koh, titled "DIAGNOSING CARDIAC HEALTH UTILIZINGPARAMETER TREND ANALYSIS"; and U.S. Patent No. 6,645,153, filed on February 7, 2002, by Kroll et al., titled "SYSTEM AND METHOD FOR EVALUATING RISK OF MORTALITY DUE TOCONGESTIVE HEART FAILURE USING PHYSIOLOGIC SENSORS", the entire content of which is incorporated herein by reference.

[0057] When used in connection with data, signals, information, etc., the terms "obtain" and "acquire" include at least one of the following: i) accessing the memory of an external device or a remote server that stores data, signals, information, etc., ii) receiving data, signals, information, etc. via a wireless communication link between the IMD and a local external device, and / or iii) receiving data, signals, information, etc. on a remote server via a network connection. When viewed from the perspective of the IMD, the obtaining operation may include real-time sensing of new signals, and / or accessing the memory to read stored data, signals, information, etc. from the memory within the IMD. When viewed from the perspective of the local external device, the obtaining operation includes receiving data, signals, information, etc. at the transceiver of the local external device, where the data, signals, information, etc. are sent from the IMD and / or the remote server. The obtaining operation can be viewed from the perspective of the remote server, such as when receiving data, signals, information, etc. at the network interface from the local external device and / or directly from the IMD. The remote server can also obtain data, signals, information, etc. from local memory and / or other memories (such as in a cloud storage environment) and / or from the memory of a workstation or a clinician's external programmer.

[0058] The terms "artificial intelligence", "machine learning", and "self-learning" can be used interchangeably throughout and refer to artificial intelligence algorithms that learn from various automatic or manual inputs (such as features of interest, previously device-classified arrhythmias, observations, and / or data). The machine learning algorithms are adjusted in multiple iterations based on features of interest, postures, HS signals, S1 COM, S2 COM, EMAT, SI, CA signals, features of interest of CA signals, previously device-classified arrhythmias, observations, and / or data. For example, the machine learning algorithms are adjusted through supervised learning, unsupervised learning, and / or reinforcement learning. Non-limiting examples of machine learning algorithms are convolutional neural networks, gradient-boosted random forests, decision trees, K-means, deep learning, artificial neural networks, etc.

[0059] The term "subcutaneous" means under the skin but not intravenously. For example, subcutaneous electrodes / leads do not include electrodes / leads located in the heart chambers, cardiac veins, or the branches or tributaries of the coronary sinus.

[0060] The terms "RA", "LA", "RV", and "LV" refer to the right atrium, left atrium, right ventricle, and left ventricle, respectively.

[0061] The term "leadless" generally refers to a conductive lead that does not pass through a blood vessel or other anatomical structure outside the intracardiac space, while "intracardiac" generally refers to being entirely within the heart and associated blood vessels, such as the superior vena cava (SVC), inferior vena cava (IVC), coronary sinus (CS), coronary vein (CV), pulmonary artery, etc.

[0062] The term "COI" refers to a feature of interest in the CA signal. Non-limiting examples of the COI from the PQRST complex include the R wave, P wave, T wave, and isoelectric segment. Non-limiting examples of the COI from the CA signal collected on a single one or more electrodes include sensing events (e.g., intrinsic events or evoked responses). The COI can correspond to the peak of a single sensing event, R wave, average or median P wave, R wave or T wave peak, etc.

[0063] The term "notification" refers to a communication and / or device command communicated to one or more individuals and / or one or more other electronic devices, including but not limited to network servers, workstations, laptops, tablet devices, smartphones, IMDs, devices, etc.

[0064] Overview

[0065] In accordance with new and unique aspects herein, methods and devices are described for incorporating an accelerometer into an implantable medical device (e.g., an implantable cardiac monitor (ICM)) to simultaneously record heart sound (HS) and cardiac activity (CA) signals. The methods and devices identify the center of mass (COM) of HS S1 and S2, and utilize the S1 COM and S2 COM to monitor cardiac function, such as by recording electromechanical activation time (EMAT), systolic interval (SI), diastolic interval (DI), S1-S1 interval, S2-S2 interval, S3-S3 interval, S4-S4 interval, etc. EMAT represents how electrical conduction is translated into mechanical activity. EMAT can be tracked by recording the time period between the occurrence of the QRS complex (e.g., Q wave peak) in the CA signal and the S1 COM. Additionally or alternatively, SI can be tracked by recording the time period between the S1 COM and the S2 COM.

[0066] In accordance with new and unique aspects herein, the accelerometer can represent a three-dimensional accelerometer configured to detect heart sounds along three orthogonal axes (e.g., X-axis, Y-axis, and Z-axis) relative to the device reference axis. The applicant has recognized that due to the vibratory nature of the HS signal measured by the accelerometer, there can be challenges in detecting the consistent timing of the S1 and S2 signals. To address this challenge, embodiments herein calculate the center of mass associated with each S1 heart sound and each S2 heart sound. The applicant has further recognized that there are additional challenges in determining the start and end times of the heart sounds, and more generally, in determining the position of the heart sounds on the time axis. To solve this challenge, embodiments herein utilize features of interest from the PQRST complex, such as R wave peak, Q wave peak, etc., to define and temporally locate the heart sound search windows for the S1 and S2 heart sounds.

[0067] Additionally or alternatively, to further improve the accuracy of monitoring the S1 and S2 heart sounds, filter parameters are customized for filters that process the accelerometer signals along each of the X, Y, and Z axes.

[0068] Figures 1A - 1C An example of simultaneously recorded CA signals and corresponding HS signals to be processed in accordance with embodiments herein is shown. In Figure 1A , the upper panel 101 and the lower panel 103 show electrogram (EGM) signals, as the CA signals 102, 106 recorded over a time period of slightly more than three seconds. The upper panel 101 and the lower panel 103 further show the heart sound signals 104, 108 simultaneously recorded during the same time period. The heart sound signals 104, 108 are collected along the first axis (e.g., the x-axis) of the accelerometer. The heart sound signal 104 collected in the upper panel 101 represents the signal processed using a wideband filter, while the heart sound signal 108 in the lower panel 103 represents the signal processed using a narrowband filter. By way of example, the passband of the wideband filter can be between 7.5 Hz and 100 Hz, while the passband of the narrowband filter can be between 15 Hz and 100 Hz.

[0069] In Figure 1B , the upper panel 111 and the lower panel 113 show the same CA signals 102, 106. The upper panel 111 and the lower panel 113 further show the heart sound signals 114, 118 simultaneously recorded during the same time period, but using different wideband and narrowband filters. However, the heart sound signals 114, 118 are collected along a different second axis (e.g., the Y-axis) of the accelerometer. The passbands of the wideband and narrowband filters for the Y-axis can be the same as or different from the passbands for the X-axis and / or the Z-axis.

[0070] In Figure 1C , the upper panel 121 and the lower panel 123 show the same CA signals 102, 106. The upper panel 121 and the lower panel 123 further show the heart sound signals 124, 128 simultaneously recorded during the same time period, but using different wideband and narrowband filters. However, the heart sound signals 124, 128 are collected along a different third axis (e.g., the z-axis) of the accelerometer. The passbands of the wideband and narrowband filters for the Z-axis can be the same as or different from the passbands for the X-axis and / or the Y-axis.

[0071] Figures 1A - 1C The visual comparison of the shown heart sound signals indicates that the heart sound signals will vary significantly depending on the accelerometer axis used for collection and the different filters. The filter parameters can be adjusted prior to implantation, during implantation, or at a later time during a clinical visit to achieve the desired composer output. Additionally or alternatively, one or more axes of the accelerometer can be selected to sense the HS signal based on various criteria.

[0072] Figure 2 A method for monitoring cardiac function based on heart sounds according to embodiments herein is shown. Figure 2 The operations of can be implemented by hardware, firmware, circuitry, and / or one or more processors, which are located in whole or in part within the IMD, local external device, remote server, or more generally within a healthcare system. Optionally, Figure 2 the operations of can be implemented in part by the IMD and in part by a local external device, remote server, or more generally within a healthcare system. For example, the IMD includes an IMD memory and one or more IMD processors, while each external device / system (ED) (e.g., anywhere within the local, remote, or healthcare system) includes an ED memory and one or more ED processors.

[0073] At 202, one or more processors obtain a CA signal and an HS signal during a common time period. For example, the time period can represent a predetermined number of seconds, minutes, or other, or optionally represent the number of heartbeats. The CA signal can be sensed using one or more combinations of electrodes and sensing circuitry coupled within the IMD. The HS signal can be sensed using a three-dimensional accelerometer and an HS filtering circuit within the IMD.

[0074] At 204, one or more processors identify a COI within a CA signal segment. For example, the duration of the segment can approximate the duration of a single heartbeat, and the COI can represent a Q-wave peak, an R-wave peak, or other.

[0075] At 206, one or more processors overlay S1 and S2 search windows onto corresponding HS segments of the HS signal, where the positions of the S1 and S2 search windows are determined based on the COI from the CA signal segment. For example, when the COI represents an R-wave peak, the S1 search window can be positioned to start simultaneously with the R-wave peak, or at a predetermined first interval before or after the R-wave peak. Then, the S2 search window can be positioned to start at a predetermined second interval after the R-wave peak and / or at a predetermined third interval after the end of the S1 search window. The S1 and S2 search windows each have a corresponding duration that is sufficient to span from before the start of the corresponding S1 and S2 heart sounds and extend beyond the end of the corresponding S1 and S2 heart sounds. For example, the S1 and S2 search windows can be pre-programmed to each be 250 milliseconds.

[0076] At 208, one or more processors calculate the centroid of the S1 HS and the centroid of the S2 HS to obtain S1 COM and S2COM. S1 COM represents the centroid of the S1 signal within the corresponding S1 search window. S2COM represents the centroid of the S2 signal within the corresponding S2 search window. For example, the COM can be calculated according to Equation 1 below:

[0077]

[0078] The variable HS Amp corresponds to the heart sound amplitude at the corresponding point "n" along the search window, and "n" corresponds to a time point (e.g., in milliseconds). In this example, the length of the search window corresponds to 250 data points. In other words, the COM is calculated by computing the product of i) the amplitude of the HS at each point along the search window and ii) the position of each corresponding point along the S1 or S2 search window (e.g., "n" equals 1 - 250). The products are then summed and divided by the sum of the individual HS amplitudes. More specifically, one or more processors sum the products to form a first sum, sum the amplitudes of the HS signals at each point to form a second sum; and divide the first sum by the second sum. Equation 1 is repeated for the S1 search window and the S2 search window to obtain S1 COM and S2 COM. The resulting S1 COM and S2 COM represent the first and second time points along the time lines corresponding to the CA signal and the HS signal, respectively. S1 COM also refers to the S1COM timing or the S1_COM time point. S2 COM also refers to the S2 COM timing or the S2_COM time point.

[0079] At 210, one or more processors calculate various cardiac function metrics, such as EMAT and SI. EMAT can be calculated as the interval between the R wave peak and S1 COM. For example, EM 18 can be calculated using Equation 2 (as follows):

[0080] EMAT = S1_COM - R_wave_loc, Equation 2

[0081] The variable S1_COM represents the time point of the S1 HS centroid, and the variable R_wave_loc represents the time point of the R wave peak. Additionally or alternatively, the S1_COM and R_wave_loc variables can be combined into other mathematical combinations that also represent EMAT.

[0082] As another example, SI can be calculated as the interval between S1_COM and S2_COM within a single heartbeat or cardiac cycle, e.g., using Equation 3 (as follows):

[0083] SI = S2_COM - S1_COM, Equation 3

[0084] The variable S2_COM represents the time point of the centroid of S2 HS in the current heartbeat or cardiac cycle, and the variable S1_COM represents the time point of the centroid of S1 HS in the same heartbeat or cardiac cycle. Additionally or alternatively, the S1_COM and S2_COM variables can be combined into other mathematical combinations that also represent SI. Additionally or alternatively, other intervals and times indicative of cardiac function can be calculated based on S1_COM and / or S2_COM and additional features from the CA signal. Additionally or alternatively, the diastolic interval (DI) can be calculated as the interval between S2_COM and S1_COM within a single heartbeat or cardiac cycle, for example using Equation 4 (as follows):

[0085] DI = S1_COM - S2_COM Equation 4

[0086] At 212, one or more processors combine the most recently calculated S1_COM with the aggregated set of previously calculated S1_COMs. One or more processors also combine the most recently calculated S2_COM with the aggregated set of previously calculated S2_COMs. One or more processors also combine the most recently calculated EMAT and SI with the sets of previously calculated EMAT and SI, respectively.

[0087] At 214, one or more processors determine whether to repeat the operations at 204 - 212. The operations at 204 - 212 are repeated for the CA signal and HS signal obtained within a selected time period. For example, if the time period corresponds to one minute and, with each iteration of the operations at 204 - 212, one or more processors analyze a one-second segment, then the operations at 204 - 212 will be repeated 60 times or more. Based on the decision at 214, the process branches to 216 or 218.

[0088] At 216, one or more processors shift the segment to be analyzed to the next part of the HS and CA signals. For example, when the length of the analyzed CA signal segment is 1 second, the segment can be shifted forward in time by exactly 1 second so that the next segment does not overlap with the previous segment. Alternatively, the segment can be shifted by a percentage of the segment length (e.g., 25%) so that the next segment partially overlaps with the previous segment. Thereafter, the operations at 204 - 212 are repeated for the next segment of the CA signal. The next R wave is detected, and then the positions of the next S1 and S2 search windows are defined. Within the new S1 and S2 search windows, the new S1_COM and S2_COM of the heart sounds are calculated. New EMAT and SI are calculated based on the new R wave, S1_COM, and S2_COM. Then the new values are combined with the aggregation, for example, by maintaining an average or mean over the results of each iteration from 204 - 212.

[0089] At 214, when the process determines that the entire CA signal and HS signal have been analyzed, the process moves to 218, where one or more processors store the result parameter values. Additionally, at 218, one or more processors compare the stored result parameter values with previously stored result parameter values to monitor trends.

[0090] Figure 3 A graphical example of the analysis applied to exemplary CA and HS signals at 204 - 216 is shown. In conjunction with Figure 3 The HS signal collected and presented is collected along the X-axis of the accelerometer and filtered using a narrowband filter. In Figure 3 the example, the CA signal and HS signal are collected for a period of time, such as one minute. The upper panel 302 shows a series of HS segments (e.g., 60) of the HS signal aligned with each other within a time interval. For example, the time interval can have a duration of 0.8 seconds, where consecutive HS segments of the HS signal are aligned with each other, starting at time zero and ending at a time approximately 0.75 seconds later. Each HS segment starts at a time relative to the COI from the CA signal, such as the R peak in the EGM signal. Each HS segment (e.g., 60 HS segments) is analyzed during a separate iteration of the operation of Figure 2 .

[0091] The lower panel 304 shows the aggregated (e.g., averaged) HS segment 306, which is a combination of a series of HS segments and the aggregated (e.g., averaged) CA segment 308 over a corresponding series of CA segments. The start and end aggregation search window boundaries 310, 312 define the aggregation S1 search window 314, while the start and end aggregation search window boundaries 316, 318 define the aggregation S2 search window 320. The boundaries 310, 312, 316, and 318 represent the combination (e.g., average) of the individual boundaries identified in conjunction with each individual HS segment. The start boundaries 310, 316 of the S1 and S2 search windows 314, 320 are defined in time relative to the COI of the CA signal. The aggregated S1_COM 322 is shown within the S1 search window 314. The aggregated S2_COM 324 is shown within the S2 search window 320. The S1_COM322 and S2_COM 324 are formed by combining each of the individual S1_COM and S2_COM at 212 ( Figure 2 ).

[0092] Figure 4 An example of a format in which EMAT and SI trend data can be presented is shown. The EMAT and SI trend data can be presented in accordance with Figure 4presented to a clinician or other medical personnel in the manner shown. The EMAT and SI trend data are based on HS signals collected along the X-axis of the accelerometer and filtered using a narrowband filter. In the upper panel 402, the EMAT trend data are presented by plotting the time of day along the horizontal axis and the measured duration of EMAT (in milliseconds) along the vertical axis. Each point along the trend graph represents the average of the EMAT calculated over a one-minute time period. The EMAT data are collected and presented over a longer time period (e.g., an eight-hour time interval). The EMAT data points from 15:00 to 01:00 represent measurements collected while the patient is awake, and the EMAT data points from 01:00 to 07:00 represent measurements collected while the patient is asleep. As shown, most of the EMAT data points remain within a relatively narrow range between 50 milliseconds and 130 milliseconds, which may indicate that the patient is experiencing relatively stable EMAT cardiac function.

[0093] The lower panel 404 presents the SI trend data by plotting the same time interval along the horizontal axis and the measured duration of SI (in milliseconds) along the vertical axis. As described above with respect to panel 402, each data point along the trend graph represents the average of the SI calculated over a one-minute time period. The SI data are collected and presented over a longer time period, such as an exemplary eight-hour interval. The SI data points before time 01:00 correspond to when the patient is awake, and the SI data points after correspond to when the patient is asleep. As shown, most of the SI data points remain within a relatively narrow range between 210 milliseconds and 350 milliseconds, which may indicate that the patient is experiencing relatively stable EMAT cardiac function.

[0094] Figure 5 A method for monitoring cardiac function based on heart sounds according to embodiments herein is shown. Figure 5 The operations may be implemented by hardware, firmware, circuitry, and one or more processors that are located entirely within the IMD, a local external device, or a remote server. Optionally, Figure 5 the operations may be implemented in part by the IMD and in part by a local external device and / or a remote server.

[0095] At 502, one or more processors obtain a CA signal and an HS signal during a common time period. For example, the time period may represent a predetermined number of seconds, minutes, or other, or alternatively a number of heartbeats. The CA signal may be sensed using one or more combinations of electrodes and sensing circuitry internal to the IMD or coupled to the MID. The HS signal may be sensed using a three-dimensional accelerometer and an HS filtering circuit internal to the IMD or coupled to the IMD.

[0096] At 504, one or more processors identify a COI within a current segment of the CA signal. For example, the duration of the segment can approximate the duration of a single heartbeat or some other fixed duration, and the COI can represent a Q-wave peak, an R-wave peak, or others.

[0097] At 506, one or more processors overlay S1 and S2 search windows over respective segments of the HS signal, where the positions of the S1 and S2 search windows are determined based on the COI from the CA signal segment. For example, when the COI represents an R-wave peak, the S1 search window can be positioned to start concurrently with the R-wave peak, or at a predetermined first interval before or after the R-wave peak. Then, the S2 search window can be positioned to start at a predetermined second interval after the R-wave peak and / or at a predetermined third interval after the end of the S1 search window. The S1 and S2 search windows each have a respective duration sufficient to span from before the start of the respective S1 and S2 heart sounds and extend beyond the end of the respective S1 and S2 heart sounds. For example, the S1 and S2 search windows can be pre-programmed to be 250 milliseconds each.

[0098] At 508, one or more processors combine the current S1 and S2 HS segments with an aggregate set of previously identified S1 and S2 HS segments. For example, the current S1 HS segment can be averaged with one or more previous S1 HS segments to form a running composite S1 HS segment. Similarly, the current S2 HS segment can be averaged with one or more previous S2 HS segments to form a running composite S2 HS segment. It should be recognized that averaging is only one example of a way to mathematically combine the current and previous HS segments, and other alternative mathematical combinations can be used.

[0099] At 510, one or more processors determine whether to repeat the operations at 504 - 508. The operations at 504 - 508 will be repeated for each segment of the CA signal and the corresponding segments of the HS signal obtained within the selected time period. For example, if the CA and HS signals are recorded for one minute at 502, and each iteration of 504 - 508 processes a 60-second segment of the CA and HS signals, one or more processors will analyze 60 separate combinations of the S1 and S2 HS segments, and the operations at 504 - 508 will be repeated 60 times. Based on the decision at 510, the process branches to 512 or 514.

[0100] At 512, one or more processors shift the segment to be analyzed to the next part of the HS and CA signals. For example, when the length of the CA signal segment being analyzed is 1 second, the segment can be shifted forward in time by exactly 1 second so that the next segment does not overlap with the previous segment. Alternatively, the segment can be shifted by a percentage of the segment length (e.g., 25%) so that the next segment partially overlaps with the previous segment. Thereafter, the operations at 504 - 508 are repeated for the next segment of the CA signal and the corresponding next segment of the HS signal. The next R wave is detected (at 504), and then the positions of the next S1 and S2 search windows are defined (at 506). Then the current S1 and S2 segments are combined with the S1 and S2 aggregate combinations, for example, by maintaining the average or mean S1 data value and S2 data value at each sample point or time point.

[0101] At 510, when the process determines that the entire CA signal and HS signal have been analyzed, the flow moves to 514.

[0102] At 514, one or more processors calculate the aggregate centroid of the overall / aggregate set of S1 segments to obtain the composite S1COM. One or more processors also calculate the aggregate centroid of the overall / aggregate set of S2 segments to obtain the composite S2 COM. The composite S1COM represents the centroid of the overall S1 signal over the entire recording period. The composite S2 COM represents the centroid of the overall S2 signal over the entire recording. The composite S1 COM and S2 COM can use the same equations 1, 2, and 3 as described above Figure 2 except that the underlying HS amplitude used represents the overall / mean value at each point of the composite S1 and S2 segments across multiple heartbeats, rather than a single data point of a single heartbeat.

[0103] At 516, one or more processors calculate various metrics of cardiac function, such as EMAT and SI, using the same equations 5 and 6 as above, except that the underlying data points represent the composite S1 COM, composite S2COM, and composite R wave peaks over multiple heartbeats rather than a single heartbeat.

[0104] At 518, one or more processors store / record the composite parameter values. Additionally, at 518, one or more processors compare the stored composite parameter values with previously stored composite parameter values to monitor trends.

[0105] Figure 6 Shows a graphical example of the operations in combination with Figure 5 applied to the analysis of exemplary CA and HS signals. In combination with Figure 5The HS signals collected and presented are collected along the X-axis of the accelerometer and filtered using a narrowband filter. The upper panel 602 shows a series of HS segments (e.g., 60) of the HS signal aligned with each other within a time interval, where consecutive HS segments of the HS signal are aligned with each other, starting at time zero and ending at a time approximately 0.75 seconds later.

[0106] The lower panel 604 shows a composite / aggregated (e.g., averaged) HS segment 606, which is a combination of a series of HS segments (generated at 504 - 512) and an aggregated (e.g., averaged) CA segment 608 over a corresponding series of CA segments. The start and end aggregation search window boundaries 610, 612 define a composite / aggregated S1 search window 614, while the start and end aggregation search window boundaries 616, 618 define a composite / aggregated S2 search window 620. The boundaries 610, 612, 616, and 618 represent a combination (e.g., average) of the individual boundaries identified for each individual HS segment. The start boundaries 610, 616 of the composite S1 and S2 search windows 614, 620 are defined in time relative to the COI of the CA signal (e.g., each individual R-wave or composite R-wave). An aggregated S1 COM 622 is shown within the S1 search window 614. An aggregated S2 COM 624 is shown within the S2 search window 620. The composite / aggregated S1 and S2 COM 622 and 624 are formed by performing a single corresponding COM calculation based on the composite / aggregated S1 and S2 segments.

[0107] It should be noted that Figure 2 the process of Figure 3 and the results shown in the lower panel are based on the same CA and HS signals as those Figure 5 used in the process of Figure 6 and the results shown in the lower panel. However, the search window boundary positions of the S1 search window and the S2 search window are different. For example, in Figure 3 , the outer boundary 312 of the S1 search window 314 is slightly after the 200 - millisecond time mark, while the outer boundary 612 of the S1 search window 614 is slightly before the 200 - millisecond time mark. Additionally, Figure 3 the S2 search window 320 in Figure 2 starts at 330 milliseconds and ends at approximately 540 milliseconds, while the S2 search window 620 starts at 300 milliseconds and ends at approximately 490 milliseconds. Additionally, the S1 COM and S2 COM calculated according to the process of Figure 5 are also different from the S1 COM and S2 COM calculated according to the process of Figure 2 When calculating the COM according to the process of Figure 3 , the S1 COM 322 and S2 COM 324 (inFigure 5 When calculating S1 and S2 COM during the process, S1 COM 622 and S2 COM 624 ( Figure 5 ) are located at the 90 millisecond and 400 millisecond time markers.

[0108] Optionally, Figure 2 and Figure 5 the calculations of can both be applied, or one can be selected by the doctor during implantation or during a clinical visit.

[0109] Figure 7 shows an example of EMAT and SI trend data generated by the Figure 5 process. The EMAT and SI trend data can be presented to the clinician or other medical staff in the manner shown. As described above, the upper panel 702 presents the EMAT trend data by plotting the time of day versus EMAT (in milliseconds). The lower panel 704 presents the SI trend data by plotting the same time intervals along the horizontal axis and the measurements of SI duration (in milliseconds) along the vertical axis. Figure 7

[0110] When comparing the EMAT and SI data distributions between Figure 4 and Figure 7 , subtle differences are noted. For example, Figure 4 the set of SI data points at 406 in Figure 7 is not present in the SI data set presented in Figure 4 . Additionally, Figure 7 the EMAT data set in shows a division between the data groups marked at 408 - 410 when the patient is sleeping. However,

[0111] Figure 8 shows an example application that can be implemented according to the embodiments herein. The operations on the left side of the flowchart represent operations that can be optionally performed by the IMD, while the operations on the right side represent operations that can be optionally performed by an external device. The external device can represent a local device and / or a remote external device (such as a server).

[0112] At 802, the IMD periodically initiates one or more of the processes described herein to collect CA and HS signals. For example, the IMD can collect CA and HS signals multiple times a day, such as three times a day when the patient is sleeping. As is well known, the IMD can collect CA and HS signals on other periodic or aperiodic bases, and based on certain criteria (such as other factors detected by the IMD). For example, when the patient experiences a rapid heart rate, when an arrhythmia detection algorithm detects various arrhythmias (such as atrial fibrillation, atrial tachycardia, ventricular fibrillation, ventricular tachycardia, bradycardia, sinus arrest, etc.),Figure 8 Operation. Additionally or alternatively, the process may be initiated based on changes in patient posture, changes in activity level, determination that the IMD has moved within the subcutaneous pocket, etc. For example, it may be necessary to collect separate trend data sets for different activity levels (e.g., one data set when the patient is at rest and one data set when the patient is exercising). Additionally, when one or more processors of the IMD determine that the physical location and orientation of the IMD have moved within the subcutaneous pocket, it may be necessary to collect a new trend data set. Determining that the IMD has moved within the pocket may also warrant initiating a calibration operation to determine which one or more axes of the accelerometer should be used to sense the HS signal. For example, at implantation, it may be determined to utilize data collected along the X-axis. However, at a later time point, the IMD may move within the subcutaneous pocket, and in response, it may be determined to utilize data collected along the Y-axis.

[0113] Once the measurement operation begins, the process moves to 804, where the respective electrodes, accelerometer, and respective sensing circuitry sense the CA and HS signals. After 804, various options may be implemented. For example, at 806, the IMD may simply send the raw CA and HS signals to an external device, such as the patient's phone, clinician programmer, bedside monitor, etc. As a supplement or alternative to sending the raw CA and HS signals, the process may return to 802 or continue to 808.

[0114] At 808, one or more processors identify the timing of the COI from the CA signal and calculate S1 and S2COM as described herein. The operation at 808 may implement Figure 2 the process and / or Figure 5 the process and / or its various related variations. Once S1 and S2 COM are determined, various options may be implemented. For example, at 810, the IMD may send the COI timing (e.g., the timing of the R-wave peak) along with S1 and S2 COM to an external device. At this time, the IMD may also send the raw CA and HS signals to an external device. Thereafter, the process may return to 802.

[0115] Additionally or alternatively, the process may move from 808 and / or from 810 to 812. At 812, one or more processors of the IMD calculate the EMAT and SI data values as described herein in connection with Figure 2 and 5 one or both of the processes. At 814, the IMD may send the EMAT and SI to an external device, and the process returns to 802 to await the initiation of the next measurement.

[0116] Refer to Figure 8On the right side operation, the external device can perform various operation combinations based on the information received by the external device. For example, when the raw CA and HS signals are sent to the external device, at 820, one or more processors of the external device can utilize Figure 2 and Figure 5 one or both of the processes to calculate S1 and S2 COM.

[0117] Additionally or alternatively, at 822, one or more processors of the external device can calculate EMAT and SI data values based on the COI timing, the S1 and S2 values calculated at 820, and / or wirelessly received from the IMD.

[0118] Additionally or alternatively, at 824, one or more processors of the external device can update the trend data as described herein. The update of the trend data can be based on the calculations performed by the external device at 820 and 822. Additionally or alternatively, the trend data can be updated on the EMAT and SI data directly wirelessly received from the IMD.

[0119] At 826, one or more processors of the external device analyze trend data. At 828, one or more processors of the external device determine whether a notification is needed based on the trend data. For example, the trend data may indicate a deviation from a normal pattern. As a non-limiting example, the trend data may deviate from the normal pattern when the trend data exceeds or is below upper and / or lower limits, shows a change in amplitude of a selected quantity, shows a positive or negative slope exceeding a threshold, etc. For example, EMAT and / or SI trend data may deviate from a previous trend, exceed a time threshold, or otherwise meet criteria related to notification. When it is determined that a notification is needed, the process moves to 830. At 830, one or more processors of the external device issue a notification to the patient, clinician, IMD, and / or another appropriate destination. When the notification is provided as a communication, the notification may be represented in audio, video, vibration, or other user-perceivable media. The communication may be presented in various formats, such as displaying patient information, messages, user instructions, etc. The communication is presented on one or more of the various types of electronic devices described herein and may be directed to the patient, doctor, various medical personnel, various patient record managers, etc. The communication may represent the identification of a patient diagnosis and various treatment recommendations. The diagnosis and treatment recommendations may be provided directly to the patient. For example, in some cases, the diagnosis and treatment recommendations may be to modify a dosage level, in which case the notification may be provided to a doctor or practitioner. As another example, the diagnosis and treatment recommendations may be to start, change, or end certain physical activities, in which case the notification may be provided to the patient in addition to a doctor or practitioner. Other non-limiting examples of communication type notifications include, in part or in whole, recommending scheduling an appointment with a doctor, scheduling an appointment for an additional blood test, performing additional point-of-care blood analysis at home (e.g., using a home device), recommending that the patient collect additional HS and / or IMD data. When the notification includes an action that can be performed by the patient alone, the notification may be communicated directly to the patient. Other non-limiting examples of communication type notifications include communications sent to the patient (e.g., via an electronic device), where the communication notifies the patient how the patient's lifestyle choices directly affect the patient's health. For example, when the patient consumes too much sugar, a notification may be sent to the patient informing that the excess sugar has caused spikes or offsets in the patient's S1_COM, S2_COM, EMAT, SI, etc. As another example, when the patient avoids exercise for a period of time, the notification may inform the patient that the patient's lack of exercise has triggered S1_COM, S2_COM, EMAT, and / or SI trends.

[0120] Figure 9 illustrates a graphical example of the operations of combining Figure 2 and Figure 5 applied to the analysis of exemplary CA and HS signals.

[0121] The upper panel 902 shows the start and end search window boundaries 909, 911 that define the aggregated S1 search window, while the start and end aggregated search window boundaries 915, 917 define the aggregated S2 search window. Note that the boundaries 911, 915 of the S1 and S2 search windows overlap. The aggregated / composite S1 COM 921 is shown within the S1 search window. The aggregated / composite S2 COM 923 is shown within the S2 search window.

[0122] The lower panel 904 shows the composite / aggregated (e.g., average) HS segment, which is a combination of a series of HS segments (generated at 504 - 512 in Figure 5 ) and the aggregated (e.g., average) CA segment on the corresponding series of CA segments. The start and end aggregated search window boundaries 910, 912 define the composite / aggregated S1 search window, while the start and end aggregated search window boundaries 916, 918 define the composite / aggregated S2 search window. The aggregated / composite S1 COM 922 is shown within the S1 search window. The aggregated / composite S2 COM 924 is shown within the S2 search window. The composite / aggregated S1 and S2 COM 922 and 924 are formed by performing a single corresponding COM calculation based on the composite / aggregated S1 and S2 segments.

[0123] Figure 10A and Figure 10B shows the EMAT and SI trend data collected according to Figure 9 the S1 COM and S2 COM in Figure 2 , where the S1 and S2 COM are determined by the y - axis broadband HS signal. Panels 1002 and 1004 correspond to the trend data calculated in conjunction with the process of Figure 5 (also corresponding to the upper panel 902), while panels 1006 and 1008 correspond to the trend data calculated in conjunction with the process of Figure 4 and Figure 7 (also corresponding to the lower panel 904). As shown by the EMAT and SI trend data, the HS signal collected along the y - axis in this example shows a greater distribution compared to the HS signal collected along the x - axis (discussed above in conjunction with

[0124] Although not directly shown, it is recognized that HS signals can also be collected along the z - axis of the accelerometer using broadband filters, narrow - band filters, etc. The EMAT and SI trend data can be derived from the HS signals collected along the z - axis and analyzed for their distribution relative to the HS signals collected along the y - axis (using various filters) and the x - axis (using various filters). The distribution and other characteristics of the EMAT and SI trend data can be reviewed to determine which single axis or combination of axes provides the desired metrics for the corresponding trends.

[0125] Optionally, the calibration process can be implemented automatically / periodically at the time of implantation, during subsequent clinical visits, or throughout the useful life of the IMD. The calibration process can be used to select one or more axis-specific signals (e.g., an x-axis signal, a Y-axis signal, or a z-axis signal) from the 3-D accelerometer for collecting the HS signal. Additionally or alternatively, the calibration process can identify a combination of two or more axis-specific signals to be combined to form the HS signal. For example, a composite HS signal can be formed by summing the HS signals collected along the x-axis and the y-axis. Alternative composite HS signals can be formed by summing the HS signals collected along the y-axis and the z-axis, or along the x-axis and the z-axis, or along all three of the X, Y, and Z axes. When more than one of the X, Y, and Z axis signals are used to form the composite HS signal, each individual axis-specific signal represents a sum HS signal component (e.g., an X-axis HS signal component, a Y-axis HS signal component, and a z-axis HS signal component). Additionally or alternatively, when forming the composite HS signal, a weight can be applied to each of the X, Y, and Z axis HS signal components. For example, a composite HS signal can be formed by multiplying the X-axis HS signal component by a first weight W1, multiplying the Y-axis HS signal component by a second weight W2, and then summing the products. The combination of the axis-specific signals and the corresponding weights can be determined from one or more calibration operations (e.g., under the direct supervision of a clinician) and / or automatically determined. Machine learning can be utilized to determine the axis-specific signals to be used or the combination of the axis-specific signals and the weights associated therewith.

[0126] Implantable Medical Device

[0127] Figure 11An implantable medical device (IMD) 1100 for subcutaneous implantation near the heart is shown. The IMD 1100 includes a pair of spaced-apart sensing electrodes 1114, 1126 positioned relative to a housing 1102. The sensing electrodes 1114, 1126 provide detection of far-field electrogram signals. A variety of configurations of the electrode arrangement are possible. For example, electrode 1114 may be located at the distal end of the IMD 1100, while electrode 1126 is located proximally to the IMD 1100. Additionally or alternatively, electrode 1126 may be located on opposite sides, opposite ends, or elsewhere on the IMD 1100. The distal electrode 1114 may be formed as part of the housing 1102, for example, by coating all but a portion of the housing with a non-conductive material such that the uncoated portion forms the electrode 1114. In such a case, electrode 1126 may be electrically isolated from the housing 1102 electrode by placing electrode 1126 on a component separate from the housing 1102, such as a plug 1120. Optionally, the plug 1120 may be formed as an integral part of the housing 1102. The plug 1120 includes an antenna 1128 and an electrode 1126. The antenna 1128 is configured to wirelessly communicate with an external device 1154 according to one or more predetermined wireless protocols (e.g., Bluetooth, Bluetooth Low Energy, Wi-Fi, etc.).

[0128] The housing 1102 includes various other components, such as: sensing electronics for receiving signals from the electrodes, a microprocessor for analyzing far-field CA signals, including evaluating the presence of R waves in heartbeats that occur when the IMD is in different IMD positions relative to gravity, a cyclic memory for temporarily storing CA data, a device memory for long-term storage of CA data, a sensor for detecting patient activity, including an accelerometer for detecting acceleration signals indicative of heart sounds, and a battery for powering the components.

[0129] In at least some embodiments, the IMD 1100 is configured to be placed subcutaneously using a minimally invasive method. Subcutaneous electrodes are provided on the housing 1102 to simplify the implantation process and eliminate the need for a transvenous lead system. The sensing electrodes may be located on opposite sides of the device and are designed to provide robust event detection through consistent contact at the sensor-tissue interface. The IMD 1100 may be configured to be activated by the patient or automatically activated in relation to recording subcutaneous ECG signals.

[0130] The IMD 1100 senses far-field subcutaneous CA signals, processes the CA signals to detect arrhythmias, and if an arrhythmia is detected, automatically records the CA signals in memory for subsequent transmission to an external device 1154.

[0131] The implanted location and orientation of the IMD 1100 are such that when the patient is standing, the IMD 1100 is in a reference position and orientation with respect to a global coordinate system 110 defined for the direction of gravity 12. For example, the direction of gravity 12 is along the Z-axis, and the X-axis is between the left and right arms.

[0132] As described herein, the IMD 1100 includes electrodes that collect cardiac activity (CA) signals related to multiple heartbeats and related to different IMD positions (e.g., different locations and / or different orientations). The IMD 1100 also includes one or more sensors for collecting acceleration features indicative of heart sounds generated at different points in the cardiac cycle.

[0133] Figure 12 An example block diagram of the IMD 1100 formed in accordance with an embodiment herein is shown. The IMD 1100 can be implemented to monitor ventricular activity alone or to monitor ventricular and atrial activity simultaneously through a sensing circuit. The IMD 1100 has a housing 1102 that houses electronic / computing components. The housing 1102 (which is commonly referred to as a "can," "container," "package," or "container electrode") can be programmably selected to act as an electrode for a particular sensing mode. The housing 1102 also includes a connector (not shown) that has at least one terminal 1213 and an optional additional terminal 1215. The terminals 1213, 1215 can be coupled to sensing electrodes disposed on or adjacent to the housing 1102. Optionally, more than two terminals 1213, 1215 can be provided to support more than two sensing electrodes, such as for a bipolar sensing scheme using the housing 1102 as a reference electrode. Additionally or alternatively, the terminals 1213, 1215 can be connected to one or more leads having one or more electrodes disposed thereon, where the electrodes are at different locations around the heart. The type and location of each electrode can be different.

[0134] The IMD 1100 includes a programmable microcontroller 1220 that controls various operations of the IMD 1100, including cardiac monitoring. The microcontroller 1220 includes a microprocessor (or equivalent control circuit), RAM and / or ROM memory, logic and timing circuits, a state machine circuit, and I / O circuits. The microcontroller 1220 includes an arrhythmia detector 1234 that is configured to analyze far-field cardiac activity signals to identify the presence of an arrhythmia. The microcontroller 1220 also includes an arrhythmia determination circuit 1235 for analyzing the CA signals to evaluate the presence or absence of an R-wave within a heartbeat from a first segment of the CA signals and to detect an arrhythmia based on the presence or absence of one or more R-waves of a heartbeat within a second segment of the CA signals.

[0135] The microcontroller 1220 also includes Heart Signal Analysis (HSA) processing 1237. The HSA process 1237 is configured to implement one or more operations discussed herein. The HSA process 1237 is configured as a computer-implemented method for identifying a characteristic of interest (COI) of a heartbeat from a CA signal, overlaying an HS search window onto an HS segment of the HS signal based on the COI from the CA signal, calculating the centroid (COM) of at least one of S1 or S2 HS based on the HS segment of the HS signal within the search window to obtain the corresponding at least one of S1 COM or S2 COM, and calculating at least one of an EMAT or SI data value based on at least one of S1 COM or S2 COM. The microcontroller 1220 records at least one of EMAT or SI data over time to form an EMAT trend and an SI trend.

[0136] As explained herein, the HSA process 1237 is configured to overlay S1 and S2 search windows onto the corresponding HS segments. The HSA process 1237 is configured to align the S1 search window on the HS signal to start at or near the R peak, which represents the COI. The HSA process 1237 is configured to align the S2 search window on the HS signal to start at a predetermined interval after one of the end of the S1 search window or the R peak, which represents the COI. The HSA process 1237 is configured to calculate S1COM by: calculating the product of i) the amplitude of the HS signal at a point along the S1 search window and ii) the position of the corresponding point along the S1 search window; summing the products to form a first sum; summing the amplitudes of the HS signal at these points to form a second sum; and dividing the first sum by the second sum. As explained herein, S1COM and S2 COM represent corresponding time points along the CA and HS signals. The COI occurs along the CA signal at a COI time point, and one or more processors are configured to calculate EMAT by subtracting S1 COM from the COI time point. The HSA process 1237 is configured to calculate SI as the difference between S1 COM and S2 COM.

[0137] According to embodiments herein, the microcontroller 1220 manages the storage of EMAT and SI over a period of time and monitors the trends of EMAT and SI over a period of time to indicate changes in physiological or non - physiological conditions. For example, the microcontroller 1220 (also referred to as the IMD processor) may be configured to perform all or more than one of the identification, superimposition, or calculation operations. The external device 1154 is configured to communicate wirelessly with the IMD 1100. The external device 1154 includes an ED memory and one or more ED processors. The one or more ED processors may be configured to perform at least one of the identification, overlay, and calculation operations. As an example, the external device 1154 may wirelessly receive CA and HS signals, and the one or more ED processors may perform identification, overlay, and both calculation operations.

[0138] Although not shown, the microcontroller 1220 may also include other dedicated circuits and / or firmware / software components that assist in monitoring various conditions of the patient's heart and managing pacing therapy. A switch 1226 is optionally provided to allow selection of different electrode configurations under the control of the microcontroller 1220. The switch 1226 is controlled by a control signal 1228 from the microcontroller 1220. The IMD 1100 may also be equipped with a communication modem (modulator / demodulator) 1240 to enable wireless communication. In one embodiment, the communication modem 1240 uses high - frequency modulation, such as using RF, Bluetooth, or Bluetooth Low Energy telemetry protocols. The signals are transmitted in the high - frequency range and propagate through body tissues in the liquid without stimulating the heart or being felt by the patient. The communication modem 1240 may be implemented in hardware as part of the microcontroller 1220, or as software / firmware instructions programmed into and executed by the microcontroller 1220. Alternatively, the modem 1240 may reside as a separate component separate from the microcontroller. The modem 1240 facilitates retrieving data from a remote monitoring network. The modem 1240 is capable of transmitting data directly from the patient to an electronic device used by a doctor in a timely and accurate manner.

[0139] The IMD 1100 includes a sensing circuit 1244 selectively coupled to one or more electrodes that perform a sensing operation through a switch 1226 to detect CA data indicative of cardiac activity. The sensing circuit 1244 may include a dedicated sensing amplifier, a multiplexing amplifier, or a shared amplifier. It may also employ one or more low-power precision amplifiers with programmable gain and / or automatic gain control, band-pass filtering, and threshold detection circuitry to selectively sense features of interest. In one embodiment, the switch 1226 may be used to determine the sensing polarity of the CA signal by selectively closing appropriate switches. The IMD 1100 also includes an analog-to-digital (A / D) data acquisition system (DAS) 1250 coupled to one or more electrodes via the switch 1226 to sample the CA signal of any pair of desired electrodes. The HSA process 1237 may be applied to signals from the sensing circuit 1244 and / or the DAS 1250.

[0140] For example, the external device 1154 may represent a bedside monitor installed in a patient's home for communicating with the IMD 1100 when the patient is at home, in bed, or sleeping. The external device 1154 may be a programmer used in a clinic for interrogating the IMD 1100, retrieving data, and programming detection criteria and other features. The external device 1154 may be a handheld device (e.g., a smartphone, a tablet device, a laptop computer, a smartwatch, etc.) that may be coupled to a remote monitoring service, a medical network, etc. via a network (e.g., the Internet). The external device 1154 may communicate with the telemetry circuit 1264 of the IMD via a communication link 1266. The external device 1154 facilitates a doctor's access to patient data and allows the doctor to view real-time CA signals as they are collected by the IMD 1100.

[0141] The microcontroller 1220 is coupled to the memory 1260 via an appropriate data / address bus 1262. The memory 1260 stores motion data, a baseline motion data set, CA signals, and markers and other data content associated with the detection and determination of arrhythmias.

[0142] The IMD 1100 may also include one or more physiological sensors 1270. For example, the physiological sensors 1270 may represent one or more accelerometers, such as three-dimensional (3D) accelerometers. The sensors 1270 may utilize piezoelectric, piezoresistive, and / or capacitive elements commonly used to convert the mechanical motion of a 3D accelerometer into an electrical signal received by the microcontroller 1220. By way of example, a 3D accelerometer may generate three electrical signals indicative of motion in three corresponding directions (i.e., the X, Y, and Z directions). The electrical signals associated with each of the three direction components may be divided into different frequency components to obtain different types of information therefrom.

[0143] The physiological sensor 1270 collects device position information related to gravity, while the IMD 1100 collects CA signals related to multiple heartbeats. Although shown as being included within the housing 1102, one or more physiological sensors 1270 may be located external to the housing 1102 but still be implanted within or carried by the patient.

[0144] Figure 13 A schematic illustration of a physiological sensor (e.g., physiological sensor 1270) is shown, which may be implemented as an accelerometer and is more generally referred to herein as monitoring system 1300. The monitoring system 1300 is used to detect and determine heart sound signals. In one embodiment, the monitoring system 1300 is a three-dimensional accelerometer, which may be implemented as a chip for placement within the IMD. In another embodiment, the accelerometer is formed and operates in the manner described in U.S. Patent 6,937,900 titled "AC / DC Multi-Axis Accelerometer For Determining A Patient Activity And Body Position", the complete subject matter of which is expressly incorporated herein by reference. In yet another embodiment, the accelerometer is formed and operates in the manner described in U.S. Provisional Patent Application 63 / 021,775 titled "Method and System for Heart Condition Detection Using an Accelerometer", the complete subject matter of which is expressly incorporated herein by reference. The accelerometer includes sensors that generate first (X), second (Y), and third (Z) accelerometer signals along respective X, Y, and Z axes (also referred to as the first-axis accelerometer or HS signal, the second-axis accelerometer or HS signal, and the third-axis accelerometer or HS signal). The X, Y, and Z axis accelerometer signals together define a three-dimensional or multi-dimensional (MD) accelerometer or HS data set. Although examples are described herein in connection with an accelerometer that generates accelerometer signals along three orthogonal axes, it should be recognized that embodiments may be implemented in which accelerometer signals are generated along two or more axes including more than three axes.

[0145] The monitoring system 1300 may include a sensor 1301 that monitors and receives signals from the X, Y, and Z axes. In one embodiment, the individual X, Y, and Z signals are received by a digital sampling component 1302 that receives digital inputs. Coupled to the digital sampling component 1302 is a filtering assembly 1104, which may include a digital-to-analog converter 1305, a reader device 1306, and an AC gain device 1108 for forming an alternating current (AC) signal. Although in this embodiment the filtering assembly includes the provided devices, in other examples, other devices may be used to filter the digital input signals for processing.

[0146] The monitoring system 1300 may also include an analog-to-digital conversion component 1310, and a position or direct current (DC) component. In one example, the analog-to-digital conversion component may be a 13-bit analog-to-digital converter (ADC). An evaluation version of the monitoring system 1100 may provide 3-axis (X and Y along the chip, Z perpendicular to the chip) DC-coupled attitude signals corresponding to 3 orthogonal directions and 3-axis AC-coupled activity signals. In one embodiment, each of the 6 signals may be sampled at 100 Hz and a total of 12 signals ([X / Y / Z], [Posture / Activity], [100 / 1 Hz]) may be accumulated in 1 second. The MD accelerometer data may be used to describe embodiments of the present invention.

[0147] Although about Figure 13 Described as a digital signal, but in other embodiments, the signal may be an analog signal, filtered, amplified, etc. The accelerometer data signal may be recorded in a data storage device of the accelerometer, IMD, remote device, etc., or the accelerometer data set may be obtained from the remote device or received from a storage device coupled to the accelerometer. To this end, the accelerometer data set may be a multi-dimensional accelerometer data set.

[0148] One or more embodiments generally relate to leadless IMDs and systems, such as pacemakers, implantable cardioverter-defibrillators, cardiac rhythm therapy devices, etc. As described below, embodiments utilize heart sounds to calculate one or more cardiac function (HF) indicators for a single patient, and use the HF indicators to determine one or more therapy-related (TR) delays and / or sense-related blanking intervals (SRBIs).

[0149] Figure 14 A cross-sectional view of a patient's heart 1433 is provided and shows a leadless implantable medical device (IMD) 1400. The IMD 1400 has been placed into the right atrium 1430 of the heart 1433 via the superior vena cava 1428. Figure 14 Also shown are the inferior vena cava 1435 , the left atrium 1436 , the right ventricle 1437 , the left ventricle 1440 , the atrial septum 1441 separating the two atria 1430 , 1436 , and the tricuspid valve 1442 between the right atrium 1430 and the right ventricle 1437 .

[0150] An IMD 1400 is formed according to an embodiment. The IMD 1400 can represent a pacemaker, a cardiac resynchronization therapy (CRT) device, a cardioverter, a cardiac rhythm management (CRM) device, a defibrillator, and the like. The IMD 1400 includes a housing 1402 that is configured to be fully implanted into a single local chamber of the heart 1433, such as fully and only implanted into the right atrium 1430, the left atrium 1436, the right ventricle 1437, or the left ventricle 1440. Optionally, the IMD 1400 can be implanted outside the chambers of the heart but near the outer wall of the heart close to the RA, LA, RV, or LV or in a blood vessel attached to the outer wall of the heart.

[0151] The chamber into which (or closest to) the IMD 1400 is implanted is referred to as the "local" chamber. The local chamber includes a local chamber wall that physiologically responds to local activation events originating from the local chamber. The local chamber is at least partially surrounded by local wall tissue that forms, includes, or constitutes at least a part of the conduction network of the associated chamber.

[0152] As Figure 14 shown, the local chamber in which the IMD 1400 is implanted is the right ventricle 1437. For example, the IMD 1400 is mounted or fixed to the tissue wall of the right ventricle 1437 along the septum 1445 that separates the right ventricle 1437 and the left ventricle 1440. The physiological behavior of the septum 1445 wall tissue in the right ventricle 1437 may be different from that of non-septal ventricular wall tissue. Optionally, the IMD 1400 can be implanted in other regions of the RV, other chambers of the heart, in a blood vessel outside the local chamber, or implanted in the outer wall of the heart near the local chamber (e.g., through the epicardium and into the myocardium). Figure 14 The IMD 1400 in the septal wall of the RV is shown, but optionally, the IMD 1400 can be implanted at a higher position in the RV near the HIS bundle. Optionally, the IMD 1400 can be implanted in the RA or elsewhere. Alternatively, multiple IMDs can be implanted in different chambers or different parts of the same chamber of the patient's heart 1433.

[0153] The leadless IMD 1400 can sense various intrinsic events and deliver corresponding therapies based on whether subsequent intrinsic events are detected within a specific time period. For example, if no intrinsic ventricular event occurs within the programmed time period after a previous intrinsic (or paced) atrial event, the IMD can utilize one or more atrial-ventricular (AV) delays to manage ventricular pacing. Similarly, the IMD can utilize one or more ventricular-ventricular (VV) delays to manage the synchronization between right and left ventricular activities. For example, when no intrinsic right ventricular event occurs within the programmed time period after a previous intrinsic (or paced) ventricular event in the left ventricular chamber, the leadless IMD in the RV can deliver a pacing event, and vice versa. As another example, if no intrinsic event occurs at the HIS bundle within the programmed time period after a previous intrinsic (or paced) atrial event, the IMD can utilize one or more atrial-HIS (AH) delays to manage HIS bundle pacing. As another example, when the IMD is implanted in the atrium, the IMD can utilize one or more post-ventricular atrial refractory period (PVARP) blanking intervals to manage the blanking of the sensing circuit after a previous intrinsic ventricular event.

[0154] As explained herein, the leadless IMD "listens" for and detects intrinsic events based on one or more heart sounds of interest. The HS of interest is used to initiate one or more TR delays and / or SR blanking intervals. The TR delay and / or SR blanking interval is calculated based in part on the COM calculation of one or more heart sounds.

[0155] Figure 15 A side view of the IMD 1400 according to an embodiment is shown. The illustrated IMD 1400 includes a schematic diagram of some internal components of the IMD 1400. The housing 1402 of the IMD 1400 includes a first mounting end 1404, an opposite second end 1406, and an intermediate shell 1408 extending between the first end 1404 and the second end 1406. The shell 1408 is in the shape of an elongated tube and extends along a longitudinal axis 1410. The mounting end 1404 is mounted on the tissue of the inner wall of the heart chamber within the heart.

[0156] The mounting end 1404 includes an electrode 1412 that is securely attached thereto and projects outwardly from the mounting end 1404. The housing 1408 includes one or more electrodes 1426 disposed therein away from the electrode 1412. The electrodes 1412 and 1426 cooperate to define a sensing vector and sense local CA signals. The electrodes 1412 and 1426 are further configured to deliver stimulation energy to the tissue of interest. As used herein, "tissue of interest" refers to the intracardiac tissue to which the IMD 1400 is configured to monitor and deliver stimulation energy. In the illustrated embodiment, as described below, the IMD 1400 is configured to be directly attached to the tissue of interest. The electrode 1412 can be a cathode electrode actively fixed to the myocardium, while the electrode 1426 is an anode electrode. The stimulation energy can be in the form of low-energy pacing pulses, high-energy shock pulses, and the like.

[0157] When the mounting end 1404 is mounted to the intracardiac tissue, the electrode 1412 is securely fixed to and engages the tissue of interest to directly deliver stimulation energy thereto. In addition to delivering stimulation energy, in an alternative embodiment, the electrode 1412 can also be used to sense electrical activity from the tissue of interest. The electrode 1412 can be formed as a single conductive bulb, or in the form of a cone, a single wire, etc. Optionally, the electrode 1412 is not covered with an insulating material, and the conductive material is exposed to facilitate good electrical connection with the local wall tissue. Alternatively, at least a portion of the electrode 1412 is covered with an insulating layer to prevent conduction to the tissue that engages the insulating layer.

[0158] The mounting end 1404 includes a fixation element to fix the IMD in or near a local chamber of the heart. For example, the fixation element can be a fixation screw 1414 that is securely attached thereto and projects outwardly from the mounting end 104. The fixation screw 1414 is configured to extend into the tissue of interest to anchor the IMD 1400 to the intracardiac tissue. The fixation screw 1414 is configured to be screwed into the tissue to firmly adhere the IMD 1400 thereto by pressing the mounting end 1404 against the tissue and rotating the IMD 1400 in a first coupling direction. The fixation screw 1414 can be removed from the tissue by rotating the IMD 1400 in the opposite decoupling direction, combined with a slight pulling force directed away from the myocardial wall. The shape of the fixation screw 1414 can be a spiral corkscrew defining a central channel. For example, the fixation screw 1414 can surround the electrode 1412 such that the electrode 1412 is located within the central channel. In an alternative embodiment, the fixation screw 1414 is a part of the electrode 1412. For example, the electrode 1412 can have spiral threads on the outer surface of the electrode 1412 such that the electrode 1412 forms the fixation screw 1414.

[0159] Additionally or alternatively, the fixation element may include one or more rings, flaps, etc., configured to hold the IMD in a blood vessel, septal wall, or other tissue near the local chamber of the heart. For example, one or more of the formations of the IMD and / or fixation element may be as in U.S. Published Application 2019 / 0099087, titled "Wireless Sensor for Measuring Pressure", published on April 4, 2019; U.S. Patent 9,993,167, titled "Apparatus and Method for Sensor Deployment and Fixation", issued on June 12, 2018; U.S. Published Application 2016 / 0007924, titled "Implantable Pressure Transducer System Optimized to Correct Environmental Factors", published on January 14, 2016, the entire subject matter of which is hereby incorporated by reference in its entirety.

[0160] The housing 102 holds the power source 1416 and various electronic components that receive current from the power source 1416. The electronic components provide the functions of the IMD 1400, such as controlling the stimulation energy delivered to the electrodes 1412 and sensing depolarization along the tissue of interest in response to pacing pulses or intrinsic heartbeats. The power source 1416 stores charge to be gradually distributed to the electronic components as needed. The power source 1416 may be a battery. The power source 1416 has a fixed charge at full capacity. The power source 1416 may be rechargeable in some embodiments and non-rechargeable in other embodiments. The power source 1416 is entirely held within and surrounded by the housing 1402.

[0161] The electronic components include a pulse generator 1418, a processor 1420, a memory 1422, a sensing circuit 1424, and a monitoring sensor / system 1425, such as the monitoring system 1300 ( Figure 13 ) and / or the physiological sensor 1270 ( Figure 12 ). This illustration is only for an overview of the electronic components and the electronic components according to an embodiment of the IMD 1400. The pulse generator 1418 provides stimulation energy to the electrodes 1412, which is delivered to the tissue of interest to which the electrodes 1412 are coupled. The pulse generator 1418 includes circuitry for controlling the output of the stimulation energy directed to the electrodes 1412. For example, the pulse generator 1418 generates lower energy pulses for pacing and higher energy pulses for defibrillation.

[0162] Processor 1420 is a controller that controls the flow of charge between power source 1416, electronic components (such as pulse generator 1418, monitoring sensor 1425, and sensing circuit 1424), and electrodes (such as electrode 1412). For example, processor 1420 controls the timing and intensity or amplitude of the stimulation pulses. If multiple electrodes are used to deliver stimulation energy to the cardiac tissue, processor 1420 can synchronize the delivery of the pulses. Processor 1420 is communicatively coupled to pulse generator 1418, sensing circuit 1424, memory 1422, and power source 1416. Processor 120 also acts based on instructions locally stored in memory 1422. Memory 1422 is a non-transitory tangible computer-readable storage medium. Memory 1422 stores programmable and executable instructions for processor 1420. Processor 1420 responds to the programmable instructions to control the operation of IMD 1400 as described herein. Memory 1422 can also store data. Some data can be stored before the completion of the IMD 1400 assembly, while other data can be stored during the use of the implanted IMD 1400. For example, memory 1422 can be used to store data regarding the intrinsic electrical activity within the heart monitored by sensing circuit 1424, data regarding the number, timing, and / or amplitude of the pacing pulses generated by pulse generator 1418, etc. Memory 1422 is also configured to store HS signals, S1-COM, S2-COM, S3-COM, S4-COM, EMAT, SI, DI, etc.

[0163] Sensing circuit 1424 is configured to monitor the intrinsic electrical CA signals within the heart. Sensing circuit 1424 is communicatively coupled to one or more sensing electrodes 1412, 1426 located on or extending from housing 102. The illustrated sensing electrodes 1426 are arranged along one or more sides of housing 1408, but can additionally or alternatively be arranged along the second end 1406 of housing 1402. Sensing electrodes 1426 sense electrical activity, such as physiological and pathological behaviors and events, and in response provide a sensing signal to sensing circuit 1424. In an alternative embodiment, pulse electrode 1412 doubles as a sensing electrode such that pulse electrode 1412 is used to deliver stimulation pulses and, between pulses, monitors the electrical activity within the tissue of interest for sensing circuit 1424.

[0164] Optionally, the IMD 1400 may include a heart rate (HR) sensor 1427 configured to acquire HR data indicative of a patient's heart rate. For example, the HR sensor 1427 may sense a blood temperature indicative of the patient's core body temperature. The processor 1420 is also configured to generate a relative temperature signal based on the blood temperature signal. The processor 1420 also generates a moving baseline temperature signal based on the relative temperature signal, generates a proportional response signal based on the relative temperature signal and the moving baseline temperature signal, and generates a sensor-indicated rate response signal based on the proportional response signal and a base rate. The sensor-indicated rate response signal may also be based on a falling response signal and / or a slope response signal. When in a therapy mode, the processor 1420 is configured to adjust at least one of the TR delay or the SR BI based on the HR data. For example, the processor 1420 may be configured to adjust one or more pacing parameters to control a pacing rate based on the sensor-indicated rate response signal. For example, the processor 1420 may adjust an AV delay, a VV delay, a PVARP blanking period, etc. based on the sensor-indicated rate response signal.

[0165] Figure 16 A method of monitoring cardiac function based on heart sounds to define one or more therapy-related delays and / or one or more sensing-related BIs according to embodiments herein is shown. Figure 16 The operations of may be implemented when the IMD is in a calibration mode and may be implemented by hardware, firmware, circuitry, and / or one or more processors, which are located in whole or in part within the IMD, a local external device, a remote server, or more generally within a healthcare system. Optionally, Figure 16 the operations of may be implemented in part by the IMD and in part by a local external device, a remote server, or more generally within a healthcare system. For example, the IMD includes an IMD memory and one or more IMD processors, while each external device / system (ED) (e.g., anywhere within the local, remote, or healthcare system) includes an ED memory and one or more ED processors.

[0166] At 1602, one or more processors acquire a CA signal and an HS signal during a common time period. For example, the time period may represent a predetermined number of seconds, minutes, or other, or optionally a number of heartbeats. The CA signal may be sensed using one or more combinations of electrodes and sensing circuitry coupled within the IMD. The HS signal may be sensed using a monitoring sensor / system 1425 (e.g., a three-dimensional accelerometer and an HS filtering circuit) within the IMD.

[0167] At 1604, one or more processors identify a COI within a CA signal segment. For example, the duration of the segment may approximate the duration of a single heartbeat, and the COI may represent a Q-wave peak, an R-wave peak, or other.

[0168] In 1606, one or more processors overlay an HS search window over a corresponding HS segment of the HS signal, where the position of the HS search window is determined based on the COI from the CA signal segment. For example, the HS search window may correspond to S1 and S2 heart sounds. Additionally or alternatively, the Hs search window may correspond to S3 and S4 heart sounds, and / or any combination of S1, S2, S3, and S4 heart sounds. When the COI represents the R wave peak, the S1 search window may be positioned to start simultaneously with the R wave peak, or at a predetermined first interval before or after the R wave peak. Then, the S2 search window may be positioned to start at a predetermined second interval after the R wave peak and / or at a predetermined third interval after the end of the S1 search window. The S3 and / or S4 search windows may be positioned to start at corresponding predetermined intervals after the R wave peak and / or at corresponding predetermined intervals after the end of the S1 and / or S2 search windows. The S1, S2, S3, and S4 search windows each have a corresponding duration that is sufficient to span from before the start of the corresponding S1, S2, S3, and S4 heart sounds of interest and extend beyond the end of the corresponding S1, S2, S3, and S4 heart sounds of interest. For example, the S1, S2, S3, and S4 search windows may be pre-programmed to be 250 milliseconds each or have different durations.

[0169] In 1608, one or more processors calculate the centroid of at least one heart sound of interest to obtain a corresponding at least one HS COM. For example, HS COM may be calculated for S1 and S2 heart sounds to obtain S1 COM and S2 COM. Additionally or alternatively, COM may be calculated for S3 and S4 heart sounds to obtain S3 COM and S4 COM. As described above, COM represents the centroid of the corresponding HS signal within the corresponding search window. The calculation of COM may utilize Equation 1 discussed above, but for the corresponding search window. In the above example, the length of the search window corresponds to 250 data points. Optionally, search windows for different types of HS may have different lengths (e.g., the S1 search window includes 250 data points, while the S3 search window includes 300 data points). The resulting S1COM, S2 COM, S3 COM, and / or S4COM represent the first, second, third, and / or fourth time points along the timeline corresponding to the CA signal and the HS signal, respectively.

[0170] In 1610, one or more processors calculate one or more HF metrics. Examples of HF metrics include EMAT, SI, DI, S1-S1 interval, S2-S2 interval, S3-S3 interval, S4-S4 interval, S1-R peak interval, S2-R peak interval, S3-R peak interval, S4-R peak interval, etc. DI is calculated as the difference between S1_COM and S2_COM. The S1-S1 interval is calculated as the time period between consecutive S1_COMs (e.g., S1_COM(t1)-S1_COM(t2), where t1 and t2 correspond to the time points of consecutive S1_COMs). The S2-S2 interval is calculated as the time period between consecutive S2_COMs. The S3-S3 interval is calculated as the time period between consecutive S3_COMs. The S4-S4 interval is calculated as the time period between consecutive S4_COMs. Additionally or alternatively, the HF metric can be the interval between selected combinations of heart sounds, such as the interval between S1 and S4 (S1-S4 interval), the interval between S2 and S4 (S2-S4 interval), the interval between S3 and S4 (S3-S4 interval), etc. Additionally, or alternatively, the HF metric can be the interval between HS and the CA signal COI, where the CA signal COI is in the same / current cardiac cycle as HS, or the CA signal COI is in the next cardiac cycle after HS. For example, the HF metric can be the interval between S1, S2, S3, or S4 and the R peak of the next consecutive cardiac cycle (e.g., S1-R peak, S2-R peak, S3-R peak, S4-R peak). For example, the S4 to R peak interval can be calculated as interval = S4_COM - R_wave_loc, where R_wave_loc represents the time point at which the R peak occurs.

[0171] In 1612, one or more processors combine the most recently calculated HF metric with an aggregated set of previously calculated HF metrics. For example, one or more processors combine the most recent DI with the aggregated set of previously calculated DIs. Additionally or alternatively, one or more processors combine the most recent S4-R peak interval with the aggregated set of previously calculated S4-R peak intervals. Additionally or alternatively, one or more processors combine the most recently calculated S4-S4 interval with the aggregated set of previously calculated S4-S4 intervals, and / or combine the most recently calculated S2-R peak interval with the aggregated set of previously calculated S2-R peak intervals.

[0172] It is recognized that multiple different types of HF metrics can be determined through the operations at 1604 to 1612 during a single iteration. For example, a value can be calculated for all or part of EMAT, SI, DI, S1-S1 interval, S2-S2 interval, S3-S3 interval, S4-S4 interval, S1-R peak interval, S2-R peak interval, S3-R peak interval, S4-R peak interval, etc.

[0173] In 1614, one or more processors determine whether to repeat the operations at 1604-1612. For the CA signal and the HS signal obtained within a selected time period, the operations at 1604-1612 are repeated. For example, if the time period corresponds to one minute and, through each iteration of the operations at 1604-1612, one or more processors analyze a one-second segment, the operations at 1604-1612 will be repeated 60 times or more. Based on the decision at 1614, the process branches to 1616 or 1618.

[0174] In 1616, one or more processors shift the segment to be analyzed to the next part of the HS and CA signals. For example, when the length of the analyzed CA signal segment is 1 second, the segment can be shifted forward in time by exactly 1 second so that the next segment does not overlap with the previous segment. Alternatively, the segment can be shifted by a percentage of the segment length (e.g., 25%) so that the next segment partially overlaps with the previous segment. Thereafter, the operations at 1604-1612 are repeated for the next segment of the CA signal. The next R wave is detected, and then the positions of the next S1 and S2 search windows are defined. A new HS COM is calculated for the heart sound within the corresponding HS search window. A new HF metric is calculated based on the HS COM (and optionally the CA signal COI). Then the new values are combined with an aggregation mathematics, such as maintaining an average or mean value over the results from each iteration of 1604-1612.

[0175] Optionally, when it is desired to calculate a single HF metric using a single beat, the operations at 1612-1616 can be completely omitted. In 1614, when the process determines that the entire CA signal and HS signal have been analyzed, the process moves to 1618.

[0176] In 1618, one or more processors calculate one or more TR delays and / or one or more SI BIs based on corresponding HF metrics and one or more corresponding incremental values. As explained herein, the one or more processors are further configured to manage the delivery of therapy based on at least one of the TR delay or the SR BI. For example, one or more of the TR delays may correspond to an AV delay, a VV delay, an AH delay, an HV delay, etc. The AV delay represents the delay after an intrinsic or paced atrial event before the IMD will pace the ventricle if no intrinsic ventricular event occurs. The VV delay represents the delay after an intrinsic or paced LV or RV event before the IMD will pace the contralateral ventricle if no intrinsic ventricular event occurs (e.g., an intrinsic LV event followed by a paced RV event, or vice versa). The AH delay represents the delay after an intrinsic or paced atrial event before the IMD will pace the HIS bundle if no intrinsic HIS event occurs. The HV delay represents the delay after an intrinsic or paced HIS bundle event before the IMD will pace the ventricle if no intrinsic ventricular event occurs.

[0177] Figure 17 Illustrated is an example graph of the application of the analysis during Figure 16 the operation. The process collects a CA signal 1702 and an HS signal 1704 over a period of time (e.g., 3 seconds). During this period, the patient experiences 4 heartbeats. In conjunction with each heartbeat, a COI 1706 is identified and used to overlay one or more HS search windows onto respective HS segments of the HS signal 1704. For example, the S1, S2, and S4 search windows 1708, 1709, 1710 may overlap. If the process calculates a COM associated with an HS search window, such as an S1 COM 1712, an S2 COM 1713, and an S4 COM 1714. Figure 17 Also illustrated are S1, S2, and S4 search windows 1718, 1719, and 1720 for subsequent heartbeats, related to the process of determining an S1 COM 1722, an S2 COM 1723, and an S4 COM 1724.

[0178] At 1610, the process calculates one or more HF metrics for a series of heartbeats. For example, the HF metrics may include one or more of a DI 1730, an S1-R-peak interval 1732, an S2-R-peak interval 1734, and / or an S4-R-peak interval 1736. The HF metrics DI 1730, S1-R-peak interval 1732, S2-R-peak interval 1734, and / or S4-R-peak interval 1736 are associated with the subsequent two heartbeats.

[0179] This process is repeated and combines individual HF metrics and corresponding aggregated HF metrics. For example, DI values across a series of beats are combined to form an average DI value, an average DI value, etc. Additionally or alternatively, individual S1-R peak intervals are combined to form an aggregated S1-R peak interval. Additionally or alternatively, individual S2-R peak intervals are combined to form an aggregated S2-R peak interval. Additionally or alternatively, individual S4-R peak intervals are combined to form an aggregated S4-R peak interval. When the operations at 1604 - 1614 are complete, the process moves to 1618, where one or more aggregated HF metrics are adjusted to form / calculate the corresponding TR delay and / or SR BI.

[0180] For example, at 1618, one or more processors calculate the AV delay (or other TR delay) as the TR delay by subtracting an incremental Δ value from the aggregated DI. When using the diastolic interval (e.g., S2_COM - S1_COM), the clinician can program the Δ value to be X milliseconds. One or more processors calculate the AV delay by subtracting the Δ value from the aggregated DI.

[0181] Additionally or alternatively, one or more processors can calculate the S4-R peak interval (as shown at Figure 17 1736) as the TR delay. The S4-R peak interval can be used as the S4-R delay to start a timer when the S4 heart sound is detected. If an intrinsic ventricular event is not detected before the expiration of the S4-R delay, a pacing event can be delivered to the RV at the expiration of the S4-4 delay. Additionally or alternatively, one or more processors can calculate the S1-R peak interval (as shown at Figure 17 1732) as the TR delay. The S1-R peak interval can be used as the S1-R delay to start a timer when the S1 heart sound is detected. Additionally or alternatively, one or more processors can calculate the S2-R peak interval (as shown at Figure 17 1734) as the TR delay. The S2-R peak interval can be used as the S2-R delay to start a timer when the S2 heart sound is detected. The above examples relate to an embodiment of implanting a leadless IMD in the RV. Additionally or alternatively, the above embodiments can be applied to a leadless IMD implanted in or near the LV. In the case of LV implantation, the clinician can adjust the incremental value to account for the time between an intrinsic or paced atrial event and LV pacing.

[0182] Figure 18 Illustrated is a process for managing therapy and / or sensing by a leadless IMD based on HF metrics obtained according to embodiments herein. When the IMD is in a therapy mode and on a beat-by-beat basis, the operations in Figure 18 can be implemented.

[0183] In 1802, one or more processors of the leadless IMD manage the circuitry and other electronic components within the IMD to collect and analyze HS signals related to the current heartbeat. One or more processors analyze the HS signals to identify one or more heart sounds of interest, such as S1, S2, S3, and / or S4. The identification of the HSs of interest can include overlaying an HS search window over a segment of the HS signal.

[0184] The alignment of the HS search window can be based on the timing derived from the previous HS COM and / or previous COI within the DCA signal. For example, the current S1 search window can be positioned to start a predetermined number of milliseconds after the R-wave peak in the previous heartbeat. Additionally or alternatively, the current S2, S3, and / or S4 search windows can be positioned to start a predetermined number of milliseconds after the R-wave peak in the previous heartbeat. As another example, the HS search window can be based on the timing of the previous COM of the same heart sound. For example, over a series of beats, the average interval between consecutive S1 COMs can be determined to be X milliseconds. Thus, the current S1 search window can be timed to start X milliseconds after the start of the previous S1 search window. Similarly, the current S2, S3, and / or S4 search windows can be timed to start a few milliseconds after the start of the corresponding one of the previous S2, S3, and / or S4 search windows. As another example, the S2, S3, and / or S4 search windows can be timed to start a programmed or IMD-determined number of milliseconds after the start or end of the current S1 search window. The analysis at 1802 can simply identify the peaks of the heart sounds of interest or a more complex process of identifying the COMs of the heart sounds of interest.

[0185] In 1804, one or more processors determine whether the current segment of the HS signal includes a heart sound of interest, such as an S1 heart sound, an S2 heart sound, an S3 heart sound, and / or an S4 heart sound. When the current segment of the HS signal does not include a heart sound of interest, the process returns to 1802. This process is repeated until a heart sound of interest (e.g., S1 peak, S1 COM, S2 peak, S2 COM, S3 peak, S3 COM, S4 peak, S4 COM) is identified.

[0186] When a heart sound of interest is identified at 1804, the process moves to 1806. In 1806, one or more processors of the IMD turn on / start one or more event timers. The event timers can correspond to the TR delay and / or SR BI. For example, the event timer can correspond to an AV delay timer that is started upon detection of a heart sound of interest. For example, when the S2 heart sound is identified at 1804, the AV delay timer can be started. As described above in connection with Figure 16As described above, the AV delay is set to correspond to the DI minus the increment value programmed by the clinician. By subtracting the increment value from the DI, embodiments herein are able to estimate the time at which pacing should occur after the S2 heart sound and the current heartbeat.

[0187] The AV delay represents a delay used to manage ventricular pacing. Other TR-related delays can be similarly used to manage ventricular pacing. For example, an event timer can correspond to the S4-R delay. The S4-R delay is initiated when an S4 heart sound of interest is detected. When an S4 heart sound is identified at 1804, the S4-4 delay timer can be initiated. As described above in connection with Figure 16 what was described, the S4-R HF delay is set to correspond to the aggregated S4-R HF metric of the aqt 1612 calculated over a plurality of previous beats. The aggregated S4-RV HF metric can be used alone as the S4-R delay, or in combination with the addition / subtraction of a programmed increment value.

[0188] At 1808, one or more processors of the IMD manage the device to collect and analyze the DCA signal. At 1810, one or more processors determine whether the CA signal includes a COI indicating an intrinsic event. For example, one or more processors can determine whether the CA signal includes an R wave indicating an intrinsic ventricular contraction.

[0189] When a COI (e.g., an intrinsic ventricular contraction) is identified at 1810, the process determines that no treatment is required and the flow moves to 1812. At 1812, one or more processors reset one or more event timers. When a COI indicating an intrinsic event is not identified at 1810, the flow moves to 1814. At 1814, the process determines whether the event timer has expired. If the event timer has not expired, the flow returns to 1808 where additional CI signals are collected and analyzed. When the event timer expires at 1814, the flow continues to 1816. At 1816, the leadless IMD provides the corresponding treatment. In this example, the leadless IMD is implanted in the RV and RV pacing treatment is performed accordingly. For example, when an intrinsic ventricular event is not detected before the AV timer times out, the IMD provides ventricular treatment. Thereafter, the flow moves to 1812 where various event timers are reset. Then, the flow returns to 1802 where the leadless IMD begins collecting new heart sound signals to search for the next heart sound of interest.

[0190] The foregoing process is described in connection with a leadless IMD implanted in or near the RV or LV. When implanted within or near the ventricle, the IMD may utilize at least one TR delay that includes at least one of an HS-HS interval or an HS-R wave interval calculated by combining an incremental value with a corresponding one of the HS-HS interval or the HS-R wave interval. In this example, one or more processors are configured to: identify an HS of interest; in response to identifying the HS of interest, initiate a timer corresponding to at least one of the HS-HS interval or the HS-R wave interval; and deliver ventricular therapy when an intrinsic ventricular event is not detected before the timer expires.

[0191] Additionally or alternatively, the process may be used in conjunction with a leadless IMD implanted in or near the HIS bundle, RA, LA, and other locations. When implanted at the HIS bundle, the event timer may correspond to an H-V delay that is defined by combining (e.g., subtracting / adding) an incremental value and one or more of SI, DI, HS-HS intervals (e.g., one or more of the S1-S1 interval, S2-S2 interval, S3-S3 interval, and / or S4-S4 interval) and HS-R wave intervals (e.g., one or more of the S1-R wave interval, S2-R wave interval, S3-R wave interval, and / or S4-R wave interval), etc. Generally, "HS-HS interval" refers to one or more of the S1-S1 interval, S2-S2 interval, S3-S3 interval, and / or S4-S4 interval. Generally, "HS-R wave interval" refers to one or more of the S1-R wave interval, S2-R wave interval, S3-R wave interval, and / or S4-R wave interval. The R wave COI may be an R wave peak, R wave COM, R wave start / end, etc.

[0192] When implanted in or near the RA or LA, the event timer may correspond to a VA delay that is defined by subtracting / adding an incremental value to SI, DI, S1-S1 interval, S2-S2 interval, S3-S3 interval, S4-S4 interval, S1-R wave interval, S2-R wave interval, S3-R wave interval, S4-R wave interval, etc., where the HS of interest indicates ventricular activity and the IMD is configured to deliver an RA or LA pacing event if the VA timer expires before an intrinsic atrial event is detected.

[0193] Figure 19An alternative embodiment is shown where a leadless IMD incorporates rate adaptive mode determination of the TR delay and / or SR BI, e.g., considering patient activity. At 1902, one or more processors obtain the CA signal and the HS signal during a common time period. For example, the time period can represent a predetermined number of seconds, minutes, or the like, or alternatively, a number of heartbeats. At 1904, one or more processors calculate one or more HF metrics, e.g., by utilizing the processes described herein. At 1904, one or more processors calculate one or more TR delays and / or one or more SI BIs based on the corresponding HF metrics and one or more corresponding incremental values, e.g., by utilizing the processes described herein.

[0194] At 1906, one or more processors obtain heart rate (HR) data. The HR data can be obtained from a rate adaptive signal generated by a motion sensor (e.g., an accelerometer), a temperature sensor, etc. When using a temperature sensor, the IMD can incorporate the HS-based operation herein and implement the rate adaptive process described in U.S. Patent Application No. 17 / 393,634, titled "SYSTEM AND METHOD FOR RATE MODULATED CARDIAC THERAPY UTILIZING A TEMPERATURE SENSOR", filed on August 4, 2021, the entire subject matter of which is hereby expressly incorporated by reference in its entirety. For example, as described above, the IMD can include a temperature sensor configured to sense a blood temperature signal indicative of the patient's core body temperature. One or more processors are also configured to generate a relative temperature signal based on the blood temperature signal. One or more processors also generate a moving baseline temperature signal based on the relative temperature signal, generate a proportional response signal based on the relative temperature signal and the moving baseline temperature signal, and generate a sensor-indicated rate response signal based on the proportional response signal and a base rate. The sensor-indicated rate response signal can also be based on a falling response signal and / or a slope response signal. Additionally, the pacing rate is controlled according to the sensor-indicated rate response signal.

[0195] In 1908, one or more processors store HR data having one or more TR delays and / or one or more SR BIs. The one or more processors are configured to store HR data having at least one of a TR delay or an SR BI, such as associating a first HR with at least one of a first TR delay or a first SR BI and associating a second HR with at least one of a second TR delay or a second SR BI. For example, store a first AV delay, a first HV delay, a first PVARP BI, etc. at a heart rate of 60 bpm, and store a second AV delay, a second HV delay, a second PVARP BI, etc. at a heart rate of 90 bpm. Additionally or alternatively, heart rate ranges can be defined, such as below 50 bpm, 50 - 70 bpm, 70 - 90 bpm, etc., where each HR range is assigned a separate AV delay, PVARP blanking interval, etc.

[0196] Figure 19 The process can be repeated periodically and / or during a calibration process under the guidance of a clinician, in combination with communication with a smartphone or a home monitoring device, and independently selected by a patient, etc.

[0197] Figure 18 The process can be implemented in combination with a rate - adaptive IMD. To this end, one or more processors of the IMD obtain HR data when (1802) or after collecting and analyzing HS signals. For example, the HR data can be obtained from rate - adaptive signals generated by a motion sensor (e.g., an accelerometer), a temperature sensor, etc. The one or more processors utilize the HR data to identify one or more corresponding one or more TR delays and / or one or more SR BIs previously stored and associated with the current HR. When in a treatment mode, the one or more processors are configured to adjust at least one of the TR delay or the SR BI based on the HR data. For example, at 1806, the one or more processors can determine that the patient's HR is between 50 bpm and 70 bpm, and thus the AV delay, HV delay, PVARP BI, etc. associated with the HR range of 50 - 70 bpm should be utilized.

[0198] Embodiments may be implemented in conjunction with one or more IMDs. Non-limiting examples of IMDs include one or more of a nerve stimulation device, an implantable leadless monitoring and / or treatment device, and / or an alternative implantable medical device. For example, an IMD may represent a cardiac monitoring device, a pacemaker, a cardioverter, a cardiac rhythm management device, a defibrillator, a nerve stimulator, a leadless monitoring device, a leadless pacemaker, etc. An IMD may measure electrical and / or mechanical information. For example, an IMD may include one or more structural and / or functional aspects of one or more devices described in U.S. Patent No. 9,333,351, titled "NEUROSTIMULATION METHOD AND SYSTEM TO TREAT APNEA," issued on May 10, 2016, and U.S. Patent No. 9,044,610, titled "SYSTEM AND METHODS FOR PROVIDING ADISTRIBUTEDVIRTUAL STIMULATION CATHODE FOR USE WITH AN IMPLANTABLENEUROSTIMULATION SYSTEM," issued on June 2, 2015, which are incorporated herein by reference. An IMD may monitor transthoracic impedance, such as implemented by the CorVue algorithm provided by St. Jude Medical. Additionally or alternatively, an IMD may include one or more structural and / or functional aspects of one or more devices described in U.S. Patent No. 9,216,285, titled "LEADLESSIMPLANTABLE MEDICAL DEVICE HAVING REMOVABLE AND FIXEDCOMPONENTS," issued on December 22, 2015, and U.S. Patent No. 8,831,747, titled "LEADLESSNEUROSTIMULATION DEVICE AND METHOD INCLUDING THE SAME," issued on September 9, 2014, which are incorporated herein by reference.Additionally or alternatively, the IMD may include one or more structural and / or functional aspects of one or more devices described in U.S. Patent No. 8,391,980, titled "METHOD AND SYSTEM FOR IDENTIFYING A POTENTIAL LEAD FAILURE IN AN IMPLANTABLE MEDICAL DEVICE," issued on March 5, 2013, and U.S. Patent No. 9,232,485, titled "SYSTEM AND METHOD FOR SELECTIVELY COMMUNICATING WITH AN IMPLANTABLE MEDICAL DEVICE," issued on January 5, 2016, which patents are incorporated herein by reference. Additionally or alternatively, the IMD may be a subcutaneous IMD that includes one or more structural and / or functional aspects of one or more devices described in U.S. Application Serial No. 15 / 973,195, titled "SUBCUTANEOUS IMPLANTATION MEDICAL DEVICE WITH MULTIPLE PARASTERNAL-ANTERIOR ELECTRODES," filed on May 7, 2018; U.S. Application Serial No. 15 / 973,219, titled "IMPLANTABLE MEDICAL SYSTEMS AND METHODS INCLUDING PULSE GENERATORS AND LEADS," filed on May 7, 2018; U.S. Application Serial No. 15 / 973,249, titled "SINGLE SITE IMPLANTATION METHODS FOR MEDICAL DEVICES HAVING MULTIPLE LEADS," filed on May 7, 2018, which patents are incorporated herein by reference in their entirety. Further, according to embodiments herein, one or more combinations of the IMD may be used from the above-incorporated patents and applications. Embodiments may be implemented in conjunction with one or more subcutaneous implantable medical devices (S-IMD).For example, the S-IMD may include one or more structural and / or functional aspects of one or more devices described in U.S. Application Serial No. 15 / 973,219, filed May 7, 2018, entitled "IMPLANTABLE MEDICAL SYSTEMS AND METHODS INCLUDING PULSE GENERATORS AND LEADS"; and U.S. Application Serial No. 15 / 973,195, filed May 7, 2018, entitled "SUBCUTANEOUS IMPLANTATION MEDICAL DEVICE WITH MULTIPLE PARASTERNAL-ANTERIOR ELECTRODES", the disclosures of which are incorporated herein by reference in their entirety. The IMD may represent a passive device that utilizes an external power source and / or an active device that includes an internal power source. The IMD may provide some type of treatment / therapy, provide mechanical circulatory support, and / or only monitor one or more physiological characteristics of interest (e.g., PAP, CA signal, impedance, heart sounds). Additionally or alternatively, embodiments may be implemented in conjunction with one or more passive IMDs (PIMDs). Non-limiting examples of PIMDs may include passive wireless sensors for their own use, or incorporated into or used in conjunction with other IMDs, such as cardiac monitoring devices, pacemakers, cardioverters, cardiac rhythm management devices, defibrillators, nerve stimulators, leadless monitoring devices, leadless pacemakers, replacement valves, shunts, grafts, drug eluting devices, blood glucose monitoring systems, orthopedic implants, and the like. For example, embodiments may implement one or more structural and / or functional aspects of one or more devices described in U.S. Patent No. 9,265,428, entitled "Implantable Wireless Sensor"; U.S. Patent No. 8,278,941, entitled "Strain Monitoring System and Apparatus"; U.S. Patent No. 8,026,729, entitled "System and Apparatus for In-Vivo Assessment of Relative Position of an Implant"; U.S. Patent No. 8,870,787, entitled "Ventricular Shunt System and Method"; and U.S. Patent No. 9,653,926, entitled "Physical Property Sensor with Active Electronic Circuit and Wireless Power and Data Transmission", the disclosures of which are incorporated herein by reference in their respective entireties.

[0199] Additionally or alternatively, embodiments herein may be implemented in conjunction with the methods and systems described in U.S. Application No. 16 / 869,733, "METHOD AND DEVICE FOR DETECTING RESPIRATION ANOMALY FROM LOW - FREQUENCY COMPONENT OF ELECTRICAL CARDIAC ACTIVITY SIGNALS", filed on the same day as this application (Docket No. 13964USO1) (13 - 0396US01), which is hereby incorporated by reference in its entirety.

[0200] Additionally or alternatively, embodiments herein may be implemented in conjunction with the methods and systems described in Provisional Application No. 17 / 192,961, "SYSTEM FOR VERIFYING A PATHOLOGIC EPISODE USING AN ACCELEROMETER", filed on March 5, 2021 (Docket No. 13967USO1) (13 - 0397US01), which is hereby incorporated by reference in its entirety.

[0201] All references cited herein, including publications, patent applications, and patents, are hereby incorporated by reference to the same extent as if each reference were individually and expressly indicated to be incorporated by reference and set forth in full herein.

[0202] Conclusion

[0203] It should be clearly understood that the various arrangements and processes described and illustrated extensively with respect to the figures, and / or one or more individual components or elements of such arrangements, and / or one or more process operations associated with such processes, may be used independently of or in conjunction with one or more other components, elements, and / or process operations described and illustrated herein. Accordingly, although the various arrangements and processes are extensively contemplated, described, and illustrated herein, it should be understood that they are provided only in an illustrative and non - limiting manner and may also be regarded as merely examples of a possible working environment in which one or more arrangements or processes may operate or function.

[0204] As will be understood by those skilled in the art, various aspects may be embodied as a system, a method, or a computer (device) program product. Accordingly, aspects may take the form of an entirely hardware embodiment, or an embodiment including both hardware and software, which may generally be referred to herein as "circuitry", "module", or "system". In addition, aspects may take the form of a computer (device) program product embodied in one or more non-transitory computer (device) readable storage media of one or more computers (devices) having computer (device) readable program code embodied thereon.

[0205] Any combination of one or more non-transitory computer (device) readable media may be utilized. A non-transitory medium may be a storage medium. A storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a storage medium include: a portable computer diskette, a hard disk, a random access memory (RAM), a dynamic random access memory (DRAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0206] The program code for performing the operations may be written in any combination of one or more programming languages. The program code may execute entirely on a single device as a stand-alone software package, partly on a single device, partly on a single device and partly on another device, or entirely on another device. In some cases, the devices may be connected through any type of network connection, including a local area network (LAN) or a wide area network (WAN), or may be connected through other devices (e.g., through the Internet using an Internet service provider) or by a hardwired connection (e.g., through a USB connection). For example, a server having a first processor, a network interface, and a storage device for storing code may store the program code for performing the operations and provide the code to a second device having a second processor for executing the code on the second device via the network through its network interface.

[0207] This document describes various aspects with reference to the accompanying drawings, which illustrate example methods, devices, and program products according to various example embodiments. These program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device or information processing device to produce a machine such that the instructions executed by the processor of the device implement the specified functions / actions. The program instructions can also be stored in a device-readable medium, which can direct the device to operate in a specific manner such that the instructions stored in the device-readable medium produce an article of manufacture including instructions for implementing the specified functions / actions. The program instructions can also be loaded onto the device to cause a series of operational steps to be performed on the device to produce a process implemented by the device such that the instructions executed on the device provide a process for implementing the specified functions / actions.

[0208] The units / modules / applications herein can include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), logic circuits, and any other circuit or processor capable of performing the functions described herein. Additionally or alternatively, the modules / controllers herein can represent circuit modules of hardware that can be implemented with associated instructions (e.g., software stored on a tangible and non-transitory computer-readable storage medium such as a computer hard drive, ROM, RAM, etc.) for performing the operations described herein. The above examples are merely exemplary and are not intended to limit the definition and / or meaning of the term "controller" in any way. The units / modules / applications here can execute an instruction set stored in one or more storage elements to process data. The storage elements can also store data or other information as needed. The storage elements can be in the form of an information source or a physical memory element within the modules / controllers herein. The instruction set can include various commands that direct the modules / applications herein to perform specific operations, such as the methods and processes of various embodiments of the subject matter described herein. The instruction set can be in the form of a software program. The software can be in various forms, such as system software or application software. In addition, the software can be in the form of a set of individual programs or modules, program modules within a larger program, or a part of a program module. The software can also include modular programming in the form of object-oriented programming. The processing of input data by the processing machine can be in response to a user command, or in response to a result of a previous processing, or in response to a request issued by another processing machine.

[0209] It should be understood that the subject matter described herein is not limited to the construction details and component arrangements set forth in the description herein or shown in the accompanying drawings. The subject matter described herein is capable of other embodiments and can be practiced or implemented in various ways. Further, it should be understood that the language and terminology used herein are for the purpose of description and should not be regarded as limiting. The use of the terms "comprising," "including," or "having" and their variants herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0210] It should be understood that the foregoing description is illustrative and not restrictive. For example, the above embodiments (and / or aspects thereof) may be used in combination with each other. Additionally, many modifications may be made to adapt a particular situation or material to the teachings herein without departing from its scope. While the dimensions, material types, and coatings described herein are intended to define various parameters, they are by no means restrictive and are illustrative in nature. After reading the above description, many other embodiments will be apparent to those skilled in the art. Accordingly, the scope of the embodiments should be determined with reference to the appended claims and the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "comprising" and "wherein" are used as the plain English equivalents of the respective terms "including" and "wherein." Additionally, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels and are not intended to impose numerical requirements on their objects or an order of performance on their acts.

Claims

1. A system for monitoring cardiac function based on heart sound (HS), comprising: electrodes configured to sense an electrocardiogram (ECG) activity (CA) signal over a period of time; a heart sound sensor configured to sense a heart sound signal over the period of time; a memory storing specific executable instructions; one or more processors, which when executing the specific executable instructions, are configured to: identify a characteristic of interest (COI) of a heartbeat from the ECG activity signal; overlay a heart sound search window onto a heart sound segment of the heart sound signal based on the characteristic of interest from the ECG activity signal; calculate a center of mass (COM) of at least one of the S1 heart sound or the S2 heart sound based on the heart sound segment of the heart sound signal within the search window to obtain at least one of a corresponding S1 center of mass or an S2 center of mass; calculate at least one of an electro-mechanical activation time (EMAT) or a systolic interval (SI) based on at least one of the S1 center of mass or the S2 center of mass; and record at least one of the electro-mechanical activation time or the systolic interval.

2. The system according to claim 1, wherein the heart sound search window includes an S1 search window and an S2 search window, and the one or more processors are configured to overlay the S1 search window and the S2 search window onto corresponding heart sound segments.

3. The system according to claim 2, wherein the one or more processors are configured to align the S1 search window on the corresponding heart sound segment to start at or near an R-wave peak, which represents the characteristic of interest.

4. The system according to claim 2, wherein the one or more processors are configured to align the S2 search window on the corresponding heart sound segment to start at a predetermined interval after one of the end of the S1 search window or the R-wave peak, which represents the characteristic of interest.

5. The system according to claim 1, wherein the S1 center of mass and the S2 center of mass represent corresponding time points along the ECG activity signal and the heart sound signal.

6. The system according to claim 1, wherein the characteristic of interest occurs at a characteristic of interest time point along the ECG activity signal, and the one or more processors are configured to calculate the electro-mechanical activation time by subtracting the S1 center of mass from the characteristic of interest time point.

7. The system according to claim 1, wherein the one or more processors are configured to calculate the systolic interval as the difference between the S1 center of mass and the S2 center of mass.

8. The system according to claim 1, further comprising an implantable medical device (IMD), wherein the memory and the one or more processors respectively comprise a multi-dimensional implantable medical device memory and a multi-dimensional implantable medical device processor, and the multi-dimensional implantable medical device processor is configured to perform at least one of the identification, overlay, or calculation operations.

9. The system according to claim 8 further includes an external device (ED) configured to wirelessly communicate with the multi-dimensional implantable medical device, wherein the memory and the one or more processors respectively include an external device memory and an external device processor, and the external device processor is configured to perform at least one of the identification, overlay, and calculation operations.

10. The system according to claim 9, wherein the external device wirelessly receives the electrocardiogram activity signal and the heart sound signal, and the external device processor is configured to perform identification, overlay, and calculation operations.

11. The system according to claim 1, wherein the heart sound sensor includes an accelerometer configured to collect multi-dimensional (MD) accelerometer data along at least two axes, and the heart sound signal corresponds to the accelerometer data.

12. A computer-implemented method for monitoring cardiac function based on heart sound (HS), the method comprising: obtaining an electrocardiogram activity (CA) signal sensed at an implantable electrode over a period of time; obtaining a heart sound signal sensed by an implantable heart sound sensor over the period of time; under the control of one or more processors, identifying an interest feature (COI) of a heartbeat from the electrocardiogram activity signal; overlaying a heart sound search window onto a heart sound segment of the heart sound signal based on the interest feature from the electrocardiogram activity signal; calculating a centroid (COM) of at least one of the S1 heart sound or the S2 heart sound based on the heart sound segment of the heart sound signal within the search window to obtain at least one of a corresponding S1 centroid or an S2 centroid; calculating at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on at least one of the S1 centroid or the S2 centroid; and recording at least one of the electromechanical activation time or the systolic interval.

13. The method according to claim 12, wherein the heart sound search window includes an S1 search window and an S2 search window, and the one or more processors are configured to overlay the S1 search window and the S2 search window onto corresponding heart sound segments.

14. The method according to claim 13, wherein, the method includes aligning the S1 search window on the heart sound signal to start at or near an R-wave peak, which represents the interest feature.

15. The method according to claim 13, wherein, the method further includes aligning the S2 search window on the heart sound signal to start at a predetermined interval after one of the end of the S1 search window or the R-wave peak, which represents the interest feature.

16. The method according to claim 14, wherein the calculating of the S1 centroid comprises: calculating the product of: i) the amplitude of the heart sound signal at a point along the S1 search window and ii) the position of the point along the S1 search window; summing the products to form a first sum; summing the amplitudes of the heart sound signal at the points to form a second sum; and dividing the first sum by the second sum.

17. The method according to claim 12, wherein the feature of interest occurs at a feature-of-interest time point along the electrocardiographic activity signal, and the method calculates the electromechanical activation time by subtracting the S1 centroid from the feature-of-interest time point.

18. The method according to claim 12, further comprising storing the electromechanical activation time and the systolic interval over a period of time, and monitoring the trends of the electromechanical activation time and the systolic interval over a period of time to indicate changes in physiological or non-physiological conditions.

19. The method according to claim 12, further comprising wirelessly transmitting the electrocardiographic activity signal and the heart sound signal from an implantable medical device (IMD) to an external device (ED), and the external device performs at least one of the operations of identification, masking, calculation, and recording.

20. The method according to claim 12, wherein the operation of identification, masking, or calculation is implemented by an implantable medical device.

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