Pacesetter, Inc.

Badie, Nima

Folman, Carin

Goil, Aditya

Rhude, Jennifer

Aditya Goil

Nima Badie

Jan O Mangual-Soto

Wenwen Li

Luke C McSpadden

Gene A Bornzin

Kyungmoo Ryu

Fujian Qu

Fady Dawoud

Carin Folman

Kevin J Davis

Xiaoyi Min

Stuart Rosenberg

Didier Theret

Yun Qiao

Jennifer Rhude

Julie Prillinger

Pritika Toutam

Gabriel A Mouchawar

Thomas Flavian Strange

Xing Pei

Richard P Williamson

Joseph Beauvais

Avi Fischer

Mostafa Sadeghi

Leyla Sabet

Christopher Gloschat

Luke C Mcspadden

Daniel F Booth

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.

Methods and systems for predicting arrhythmia risk utilizing machine learning models

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.

Methods and systems for arrhythmia episode prioritization and improving arrhythmia detection and classification to reduce clinical review burden

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.

Systems and methods for automated capture threshold testing and associated his bundle pacing

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.

Method and device for detecting abnormal battery consumption due to extra-battery mechanisms

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.

Method and system utilizing a percentage-based atrio-ventricular delay adjustment

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. The system adjusts the candidate sensing configuration and repeat the obtain, analyze and identify operations to obtain a collection of V-A FOI relations for a corresponding collection of candidate sensing configurations and selects a resultant sensing configuration from the collection of candidate sensing configurations based on one or more criteria, the resultant sensing configuration to be utilized to manage HIS bundle pacing during an atrial arrhythmia.

Method and device for managing his bundle pacing in a non-tracking mode during AF/AT

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.

Method and system for dynamic device-based delay adjustment

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. The first capture class includes first and second capture types. The processor utilizes the second FFC COI to apply a second CC discriminator to distinguish between at least one of i) the first and second capture types within the first capture class, or ii) third and fourth capture classes and manages HIS bundle pacing based on distinctions by the first and second CC discriminators.

Systems and methods for improved his bundle and backup pacing timing

Methods, devices and program products are provided for determining an early battery depletion condition for a battery powered device. The method determines a charge consumption drawn externally from a battery cell by the device for a select period of time, obtains a measured cell voltage for the battery cell of the medical device, calculates a projected cell voltage based on the charge consumption and usage conditions, and declares an early depletion condition based on a relation between the measured and projected cell voltages.

Method and device for detecting early battery depletion condition

Methods and devices are provided for, under control of one or more processors within an implantable medical device (IMD), delivering cardiac resynchronization therapy (CRT) at one or more pacing sites. The processors obtain cardiac signals, associated with a candidate beat, from multi-site left ventricular (MSLV) electrodes distributed along a left ventricle and analyze the cardiac signals to collect at least one of a MSLV conduction pattern or a MSLV morphology. The processors compare at least one of the MSLV conduction pattern or MSLV morphology to one or more associated templates. The processors then label the candidate beat as a pseudo-fusion beat based on the comparing and adjust the CRT based on the labeling.

Method and device for discrimination of left ventricular pseudo-fusion pacing

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Stevenson Ranch, CA, United States of America

Aditya Goil