Abstract: A system includes, or is for use with, an atrial leadless pacemaker (aLP) and a ventricular leadless pacemaker (vLP) configured to communicate with one another and collectively provide DDD operation when an a2v message transmitted by the aLP is successfully received by the vLP and a v2a message transmitted by the vLP is successfully received by the aLP, during a cardiac cycle. The aLP and the vLP are also configured to collectively provide VDD operation, DDI operation or VDI operation at least some times when an a2v message transmitted by the aLP is not successfully received by the vLP, and/or a v2a message transmitted by the vLP is not successfully received by the aLP. One or more processors of the system is/are configured to determine an AV synchrony metric for the period of time, and provide one or more responses based thereon. Related methods are also described.

Abstract: A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.

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: System and method for declaring pause in cardiac activity comprises memory to store specific executable instructions and a convolutional neural network (CNN) model comprising a global average pooling (GAP) layer. One or more processors are configured to execute the specific executable instructions to obtain device classified arrhythmia (DCA) data sets generated by an implantable medical device (IMD) for corresponding candidate pause episodes declared by the IMD. The DCA data sets include cardiac activity (CA) signals for one or more beats sensed by the IMD. The processor(s) apply the CNN model to the DCA data sets to identify a valid subset of the DCA data sets that correctly characterizes the corresponding CA signals. A display is configured to present information concerning the valid subset of the DCA data sets.

Abstract: Embodiments described herein can reduce a 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 also obtained, 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. One or more arrhythmic episodes is/are selected 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 that are determined for the plurality of arrhythmic episodes. Additional and alternative embodiments are also described herein.

Abstract: An implantable medical device (IMD) and process are provided comprising one or more electrodes configured to be implanted to define a pacing vector through at least a portion of a ventricle. Sensing circuitry is configured to sense intrinsic atrial activity (As) and intrinsic ventricular activity (Vs). A pulse generator (PG) if provided, and memory configured to store program instructions and an atrioventricular delay search parameter (AVDSEARCH). The AVDSEARCH is an interval of time. One or more processors, that when executing the program instructions, is configured to direct the PG to deliver ventricular pacing pulses based on an atrioventricular delay (AVD) and periodically initiate an AVD search operation utilizing the AVDSEARCH. A heart rate is determined and compared to a threshold. Responsive to determining that the heart rate exceeds the threshold, the AVDSEARCH is reduced, and cardiac activity is detected during the AVD search operation utilizing the reduced AVDSEARCH.

Abstract: A method of pacing a His bundle of a patient heart using a stimulation system including a memory, a pulse generator, a stimulating electrode and at least one sensing electrode includes applying a plurality of impulses through the stimulating electrode to induce a plurality of responses from the patient heart. Each impulse of the plurality of impulses is delivered at a different impulse energy corresponding to a respective output setting of the stimulation system. The response characteristics for each of the plurality of responses are measured and each impulse is assigned a classification based on whether the respective response characteristics indicate capture of one or both of the His bundle and a ventricle of the patient heart. The output setting and classification for each impulse is then stored in the memory.

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 method and device for dynamic device based AV delay adjustment are provided. The method provides electrodes that are configured to be located proximate to an atrial (A) site and a right ventricular (RV) site. The method utilizes one or more processors, in an implantable medical device (IMD), for detecting an atrial paced (Ap) event or atrial sensed (As) event. The method determines a measured AV interval corresponding to an interval between the Ap event or the As event and a ventricular sensed event and calculates a percentage-based (PB) offset based on the measured AV interval. The method automatically dynamically adjusting an AV delay, utilized by the IMD, based on the measured AV interval and the PB offset and manages a pacing therapy, utilized by the IMD, based on the AV delay after the adjusting operation.

Abstract: A medical data and diagnostics management system for processing classified electrogram (EGM) datasets includes a server system that receives transmissions of classified EGM datasets, each corresponding to an arrhythmic episode detected by an implantable medical device (IMD), and applies a machine-learning model to each classified EGM dataset, thereby determining confidence indicator(s) relating to the IMD classification for each arrhythmic episode. Based upon the confidence indicator(s), the server system generates a set of machine-adjudicated EGM datasets, assigns a ranking score to each machine-adjudicated EGM dataset, and selects for display (for clinical analysis) a subset of the machine-adjudicated EGM datasets based upon their ranking scores. The machine-adjudicated EGM datasets are also stored in a database and further processed to generate diagnostic information and/or diagnostic alerts relating to the arrhythmic episodes detected by the IMD over time.

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 indicative 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: System and methods are provided herein and include a HIS electrode configured to be located proximate to a HIS bundle and to at least partially define a HIS sensing vector. They system includes memory to store program instructions and cardiac activity (CA) signals for a series of beats utilizing a candidate sensing configuration. The candidate sensing configuration is defined by i) the HIS sensing vector and ii) a sensing channel that utilizes sensing circuitry configured to operate based on one or more sensing settings to detect near field and far field activity. The system includes one or more processors that, when executing the program instructions, are configured to analyze the CA signals to obtain an atrial (A) feature of interest (FOI) and a ventricular (V) FOI for the corresponding beats within the series of beats and identify a V-A FOI relation between the A FOIs and the V FOIs across the series of beats.

Abstract: A method and device for dynamic device based AV delay adjustment are provided. The method provides electrodes that are configured to be located proximate to an atrial (A) site and a right ventricular (RV) site. The method utilizes one or more processors, in an implantable medical device (IMD), for detecting an atrial paced (Ap) event or atrial sensed (As) event. The method determines a measured AV interval corresponding to an interval between the Ap event or the As event and a ventricular sensed event and calculates a percentage-based (PB) offset based on the measured AV interval. The method automatically dynamically adjusting an AV delay, utilized by the IMD, based on the measured AV interval and the PB offset and manages a pacing therapy, utilized by the IMD, based on the AV delay after the adjusting operation.

Abstract: System and methods are provided herein and include a HIS electrode configured to be located proximate to a HIS bundle and to at least partially define a HIS sensing vector. They system includes memory to store program instructions and cardiac activity (CA) signals for a series of beats utilizing a candidate sensing configuration. The candidate sensing configuration is defined by i) the HIS sensing vector and ii) a sensing channel that utilizes sensing circuitry configured to operate based on one or more sensing settings to detect near field and far field activity. The system includes one or more processors that, when executing the program instructions, are configured to analyze the CA signals to obtain an atrial (A) feature of interest (FOI) and a ventricular (V) FOI for the corresponding beats within the series of beats and identify a V-A FOI relation between the A FOIs and the V FOIs across the series of beats.

Abstract: A device comprises external electrodes configured to be held proximate to a patient's skin and to sense conductive communication signals transmitted by an implantable medical device (IMD) within the patient. A dielectric layer is provided on at least a first electrode of the external electrodes to space the first electrode apart from the patient's skin to form a non-contact capacitively coupled interface between the patient's skin and the first electrode. The capacitively coupled interface is sensitive to the conductive communication signals. A circuit is connected to the external electrodes and is configured to output a differential signal corresponding to the conductive communication signals.

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 indicative 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 method and device for dynamic device based AV delay adjustment is provided. The method comprises electrodes that are configured to be located proximate to an atrial (A) site and a right ventricular (RV) site. The method utilizes one or more processors for detecting an atrial paced (Ap) event or atrial sensed (As) event, and measures an AV interval corresponding to an interval between the Ap event or the As event and a sensed ventricular (Vs) event. The AV interval is associated with a current heart rate (HR). The method automatically dynamically adjusts a first AV delay based directly on the measured AV interval, identifies a scale factor associated with the current HR, calculates a second AV delay by scaling the first AV delay based on the scale factor and manages a pacing therapy, utilized by the IMD, based on the first and second AV delays.

Abstract: A system and method are provided. The system includes a HIS electrode configured to be located proximate to a HIS bundle. A pulse generator is coupled to the HIS electrode and is configured to deliver HIS bundle pacing (HBP), a right atrial (RA) electrode is located in a right atrium, a sensing circuitry coupled to the RA electrode and defines an RA sensing channel that does not utilize the HIS electrode. The system includes a memory including program instructions. The system includes a processor is configured to collect cardiac activity (CA) signals over the RA sensing channel utilizing the RA electrode. The CA signals include a far field (FF) component associated with a ventricular event (VE). The processor analyzes the FF component to identify first and second FF component (FFC) characteristics of interest (COI) of the ventricular event and utilizes the first FFC COI to apply a first capture class (CC) discriminator to distinguish between first and second capture classes.

Abstract: Embodiments described herein can reduce a 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 also obtained, 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. One or more arrhythmic episodes is/are selected 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 that are determined for the plurality of arrhythmic episodes. Additional and alternative embodiments are also described herein.

Abstract: A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.