Pacesetter, Inc.

Fady Dawoud

Jong Gill

Gene A Bornzin

Fujian Qu

Stuart Rosenberg

Neha Malhotra

Nima Badie

Wenwen Li

Aditya Goil

Chaoyi Kang

Xiaoyi Min

Kyungmoo Ryu

Jan O Mangual-Soto

Louis-Philippe Richer

Yun Qiao

Jan Mangual

Nikolaos Politis

Kevin J Davis

Diming Cao

Gabriel A Mouchawar

Thomas Flavian Strange

Xing Pei

Richard P Williamson

Alan B Vogel

Joseph Beauvais

Avi Fischer

Mostafa Sadeghi

Leyla Sabet

Christopher Gloschat

Kurt Salek

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.

Implantable medical devices, systems and methods for reducing T-wave oversensing and arrhythmia undersensing

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.

Method and system for monitoring heart function based on heart sound center of mass

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.

Robust ventricular sensing of far-field EGM or ECG signals that avoids oversensing of ventricular sensed events

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.

Method and system for optimizing filter settings of an implantable medical device

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.

Method and system for second pass confirmation of an arrhythmic episode

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 system and method for monitoring heart function based on heart sounds (HS) is provided. The system includes electrodes configured to sense electrical cardiac activity (CA) signals over a period of time. An HS sensor is configured to sense HS signals over the period of time. The system includes memory to store specific executable instructions and includes one or more processors that, when executing the specific executable instructions, is configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signals. The processors overlay a HS search window onto an HS segment of the HS signals based on the COI from the CA signals and calculate a center of mass (COM) for at least one of S1 or S2 HS based on the HS segment of the HS signals within the search window to obtain a corresponding at least one of S1 COM or S2 COM. The processors calculate at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on the at least one of S1 COM or S2 COM and record the at least one of the EMAT or SI.

A system for verifying a candidate pathologic episode of a patient is provided. The system includes an accelerometer configured to be implanted in the patient, the accelerometer configured to obtain accelerometer data along at least one axis. The system also includes a memory configured to store program instructions and one or more processors. When executing the program instructions, the one or more processors are configured to obtain a biological signal and identify a candidate pathologic episode based on the biological signal, analyze the accelerometer data to identify a physical action experienced by the patient, and verify the candidate pathologic episode based on the physical action.

System for verifying a pathologic episode using an accelerometer

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

Method and system to detect noise in cardiac arrhythmic patterns

Methods and systems are provided for detecting arrhythmias in cardiac activity. The methods and systems declare a current beat, from the CA signals, to be a candidate beat or an ineligible beat based on whether the current beat satisfies the rate based selection criteria. The determining and declaring operations are repeated for multiple beats to form an ensemble of candidate beats. The method and system calculate a P-wave segment ensemble from the ensemble of candidate beats, perform a morphology-based comparison between the P-wave segment ensemble and at least one of a monophasic or biphasic template, declare a valid P-wave to be present within the CA signals based on the morphology-based comparison, and utilize the valid P-wave in an arrhythmia detection process to determine at least one of an arrhythmia entry, arrhythmia presence or arrhythmia exit.

Method and system to detect P-waves in cardiac arrhythmic patterns

Systems and methods for detecting arrhythmias in cardiac activity are provided and include memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining first and second far field cardiac activity (CA) data sets over primary and secondary sensing channels, respectively, in connection with a series of beats. The system detects candidate atrial features from the second CA data set, identifies ventricular features from the first CA data set and utilizes the ventricular features to separate beat segments within the second CA data set. The system automatically iteratively analyzes the beat segments by overlaying an atrial activity search window with the second CA data set and determines whether one or more of the candidate atrial features occur within the atrial activity search window. The system adjusts an atrial sensitivity profile based on whether the atrial activity search window includes the one or more of the candidate atrial features and detects atrial events based on the atrial sensitivity profile.

Method and system for detecting arrhythmias in cardiac activity

Computer implemented methods and systems for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a far field cardiac activity (CA) data set that includes far field CA signals for beats. The method applies a feature enhancement function to the CA signals to form an enhanced feature in the CA data set. The method calculates an adaptive sensitivity level and sensitivity limit based on the enhanced feature from one or more beats within the CA data set and automatically iteratively analyzes a beat segment of interest by comparing the beat segment of interest to the current sensitivity level to determine whether one or more R-waves are present within the beat segment of interest. The method repeats the iterative analyzing operation while progressively adjusting the current sensitivity level until i) the one or more R-waves are detected in the beat segment of interest and/or ii) the current sensitivity level reaches the sensitivity limit. The method detects an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves and records results of the detecting of the arrhythmia.

Method and system to detect r-waves in cardiac arrhythmic patterns

A computer implemented method and system for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains far field cardiac activity (CA) signals and applies a direction related responsiveness (DRR) filter to the CA signals to produce DRR filtered signals. The method compares a current sample from the CA signals to a prior sample from the DRR filtered signals to identify a direction characteristic of the CA signals and defines the DRR filter based on a timing constant that is set based on the direction characteristic identified. The method analyzes the CA signals in connection with the DRR filtered signals to identify a peak characteristic of the CA signals and determines peak to peak intervals between successive peak characteristic. The method detects at least one of noise or an arrhythmia based on the peak to peak intervals and records results of the detecting.

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Santa Monica, CA, United States of America

Fady Dawoud