Abstract: Computer implemented methods and implantable medical devices (IMD) are provided that obtain cardiac activity (CA) signals for a cardiac beat and compare the CA signals to a sensitivity level to detect a sensed event. One or more processors are configured to change the sensitivity level, utilized by the sensing circuitry, over the cardiac beat based on an adaptive sensitivity profile. The adaptive sensitivity profile has a maximum sensitivity limit (MSL). The process determines whether a characteristic of interest (COI) from a candidate event satisfies criteria relative to the COI for a collection of prior sensed events, declares the candidate event to be a valid sensed event or a false sensed event based on the determine operation; and adjusts the maximum sensitivity limit based on when the COI from the candidate event satisfies the criteria to provide adaptive sensing of CA signals.

Abstract: An electrocardiogram (ECG) system includes a computing device configured to be communicatively connected to patch electrodes that are affixed to skin of a patient. The computing device includes one or more processors configured to obtain electrical signals sensed by the patch electrodes while the patch electrodes are located at actual electrode locations. The processor(s) are configured to generate initial ECG data based on the electrical signals according to assumed electrode locations on the patient that differ from the actual electrode locations. The processor(s) are configured to convert the initial ECG data to converted ECG data by determining a relationship between the assumed electrode locations and the actual electrode locations; determining voltage values for reference vectors based on the initial ECG data and the relationship; and multiplying the voltage values for the reference vectors by a set of predetermined coefficients.

Abstract: Systems, methods, and devices are provided for determining an early battery depletion (EBD) condition of an implantable medical device that includes a memory storing program instructions and a processor executing the program instructions. Circuitry is electrically coupled to the processor, and the circuitry and processor perform one or more tasks related to at least one of collecting signals indictive of physiologic activity, analyzing collected signals, delivering therapy, or communicating with an external device. A battery supplies energy to the circuitry and processor. A monitoring circuit coupled to the battery measures actual energy usage from the battery representing at least one of a current draw from the battery during corresponding tasks or a voltage measurement across the battery. Circuitry and processor calculate projected energy usage from the battery in connection with the corresponding tasks, and determine when an EBD condition exists based on projected energy usage and actual energy usage.

Abstract: A system for determining a change in position of an implanted medical device (IMD) within an implant pocket is provided. The system includes an accelerometer configured to be implanted in a patient, the accelerometer configured to obtain accelerometer data along at least one axis. The system also includes one or more processors configured to determine the patient is engaging in a determined activity over an activity period, and obtain the accelerometer data during the activity period. The one or more processors are also configured to identify postures of the patient and corresponding posture periods during the activity period based on the accelerometer data, determine a duration related to a non-standing posture identified from the postures identified, and identify a migration of the IMD within the implant pocket based on the duration of the non-standing posture exceeding a duration threshold.

Abstract: Systems, methods, and devices are provided for determining an early battery depletion (EBD) condition of an implantable medical device that includes a memory storing program instructions and a processor executing the program instructions. Circuitry is electrically coupled to the processor, and the circuitry and processor perform one or more tasks related to at least one of collecting signals indictive of physiologic activity, analyzing collected signals, delivering therapy, or communicating with an external device. A battery supplies energy to the circuitry and processor. A monitoring circuit coupled to the battery measures actual energy usage from the battery representing at least one of a current draw from the battery during corresponding tasks or a voltage measurement across the battery. Circuitry and processor calculate projected energy usage from the battery in connection with the corresponding tasks, and determine when an EBD condition exists based on projected energy usage and actual energy usage.

Abstract: A computer implemented method and system for detecting an arrhythmia are provided. The method is under control of one or more processors configured with executable instructions. The method obtains far field cardiac activity (CA) signals sensed at electrodes located remote from a heart over a period of time and applies a feature attenuation filter to the CA signals to form modified CA signals. The feature attenuation filter reduces potential T-waves as a feature not of interest. The method calculates a duty cycle (DC) characteristic of the modified CA signals with respect to duty cycle boundaries and detects an arrhythmia based on the DC characteristic.

Abstract: An implantable medical device and method are described that include a housing, a right ventricle (RV) near field (NF) electrode, a far field (FF) electrode, and sensing circuitry. The housing contains one or more processors. The RV NF electrode is electrically connected to the one or more processors and configured to be located within the RV of a heart of a patient and either in contact with ventricular myocardial tissue of the heart or within a threshold proximity of the ventricular myocardial tissue. The FF electrode is configured to be positioned beyond the threshold proximity of the ventricular myocardial tissue. The sensing circuitry is coupled to the RV NF electrode and the FF electrode and configured to define an atrial sensing vector to collect atrial sensing data associated with atrial cardiac activity. The one or more processors are configured to receive the atrial sensing data.

Abstract: A method for controlling an implantable medical device (IMD) is provided. The method can include obtaining, with a monitoring device, cardiac activity signals, and obtaining, with an accelerometer, candidate heart sound (HS) signals. The method can also include analyzing the cardiac activity signals based on an initial criteria and eliminating candidate HS signals based on the analyzing to provide remainder candidate HS signals. The method also can include analyzing the remainder candidate HS signals based on HS quality criteria related to the remainder candidate HS signals, eliminating additional candidate HS signals from the remainder candidate HS signals based on analyzing the remainder candidate HS signals to provide a HS ensemble, and controlling the IMD based on the HS ensemble. An IMD is also provided.

Abstract: A system for monitoring a physiologic condition of a patient includes an implantable medical device (IMD) comprising: an accelerometer configured to output an accelerometer signal; sensing circuitry configured to sense a cardiac activity (CA) signal; and a memory configured to store program instructions. One or more processors that, when executing the program instructions, are configured to: determine a heart rate (HR) at a point in time based on the CA signal; determine an activity level (AL) at the point in time based on accelerometer data that is based on the accelerometer signal, the HR and AL forming an HR-AL event; compare the HR-AL event to a heart condition (HC) metric that defines combinations of HRs and ALs representing a physiologically normal heart condition and a physiologically abnormal heart condition; and transmit the HR-AL event or a result of the comparison to a second device for use in diagnosing and/or treating a heart condition.

Abstract: Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrythmia undersensing that occur due to inappropriate filtering of a signal indicative of cardiac electrical activity. A method includes obtaining a signal indicative of cardiac electrical activity, and using a first bandpass filter to produce a first filtered version thereof, using a second bandpass filter to produce a second filtered version thereof, wherein the first bandpass filter passes frequencies within a first frequency range, and the second bandpass filter passes frequencies within a second frequency range that is wider than the first frequency range. The method also includes selectively changing from using the first filtered version of the signal to monitor for a VS event, to using the second filtered version of the signal to monitor for a VS event, based on first criteria, and vice versa, based on second criteria.

Abstract: A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals 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.

Abstract: Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrhythmia undersensing that occur due to inappropriate filtering of a signal indicative of cardiac electrical activity. A method includes obtaining a signal indicative of cardiac electrical activity, and using a first bandpass filter to produce a first filtered version thereof, using a second bandpass filter to produce a second filtered version thereof, wherein the first bandpass filter passes frequencies within a first frequency range, and the second bandpass filter passes frequencies within a second frequency range that is wider than the first frequency range. The method also includes selectively changing from using the first filtered version of the signal to monitor for a VS event, to using the second filtered version of the signal to monitor for a VS event, based on first criteria, and vice versa, based on second criteria.

Abstract: System and method for declaring arrhythmias in cardiac activity are provided. The system includes memory to store specific executable instructions and a machine learning (ML) model. One or more processors are configured to execute the instructions to obtain device classified arrhythmia (DCA) data sets generated by an implantable medical device (IMD) for corresponding candidate arrhythmias episodes declared by the IMD. The DCA data sets include cardiac activity (CA) signals for one or more beats sensed by the IMD and one or more device documented (DD) markers generated by the IMD. The system applies the ML model to the DCA data sets to identify a valid sub-set of DCA data sets that correctly characterize the corresponding CA signals and an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals. A display is configured to present information concerning at least one of the valid sub-set or invalid sub-set.

Abstract: A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals 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.

Abstract: Described herein are methods, devices, and systems that identify ventricular sensed (VS) events from a signal indicative of cardiac electrical activity, such a far-field EGM or ECG signal, and monitor for an arrythmia and/or perform arrythmia discrimination based on the VS events. Beneficially, such embodiments reduce the probability of double-counting of R-wave, or more generally, of oversensing VS events, and thereby provide for improved arrythmia detection and arrythmia discrimination.

Abstract: A system and a method include an implantable medical device (IMD) having one or more inputs configured to receive one or more sensed signals from one or more electrodes. A plurality of filters are configured to filter the one or more sensed signals and output a plurality of filtered signals. Memory is configured to store program instructions. A processor, when executing the program instructions, is configured to receive the plurality of filtered signals, and analyze the plurality of filtered signals to determine a desired one of the plurality of filters.

Abstract: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

Abstract: A system for monitoring a physiologic condition of a patient including an accelerometer configured to be implanted in the patient, the accelerometer configured to obtain multi-dimensional (MD) accelerometer data along at least two axes. When executing program instructions, the one or more processors may be configured to initiate a data collection interval in connection with patient activity, obtain the MD accelerometer data during the patient activity for at least one of a select period of time or until receiving a data collection halt instruction, and calculate a travel-related (TR) parameter based on the MD accelerometer data. The one or more processors may also be configured to correlate the TR parameter with physiologic data obtained during the patient activity, and store the TR parameter and physiologic data as indicators of a current physiologic state of the patient.

Abstract: Embodiments reduce burden associated with analyzing EGM segments obtained from an IMD that monitors for arrhythmic episodes. Respective EGM data and respective classification data is obtained for each arrhythmic episode detected by the IMD during a period of time. A representative R-R interval or HR for each of the arrhythmic episodes is determine by the IMD, wherein a manner for determining the representative R-R interval or HR depends on the type of the arrhythmic episode, such that for at least two different types of arrhythmic episodes the manners differ. An external system(ES) obtains data from the IMD, directly or indirectly, and selects arrhythmic episode(s) for which corresponding EGM segments are to be displayed for each type of arrhythmic episode, wherein the selecting is performed based on the representative R-R intervals or HRs determined by the IMD for the arrhythmic episodes. Additional and alternative embodiments are also described herein.

Abstract: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.