Pacesetter, Inc.

Tc1 LLC

Jong Gill

Gene A Bornzin

Fujian Qu

Xiaoyi Min

Peter Boileau

Stuart Rosenberg

Kyungmoo Ryu

Fady Dawoud

Nima Badie

Neha Malhotra

Joseph J Florio

Taraneh Ghaffari Farazi

Brian Jeffrey Wenzel

Rupinder Bharmi

Michael E Benser

Jan O Mangual-Soto

Louis-Philippe Richer

Luke C McSpadden

Annapurna Karicherla

Euljoon Park

Yelena Nabutovsky

Edward Karst

Cecilia Qin Xi

William Hsu

Wenbo Hou

Allen J Keel

Mark W Kroll

Jeffery D Snell

Timothy A Fayram

Gabriel A Mouchawar

Eric Falkenberg

Laurence S Sloman

Wenwen Li

Dean P Andersen

Mohssen Fard

Nikolaos Politis

Chunlan Jiang

Riddhi Shah

Cem M Shaquer

Jay Snell

Bing Zhu

Ryan Rooke

Avi Fischer

Laleh Jalali

Josh Reiss

George L Walls

Falkenberg Eric

Xiaozheng Zhang

Chunlei Ke

Alexander Huemmer

Philip B Adamson

Prakrit Shrestha

System for arrhythmia detection and confirmation includes implantable medical device (IMD) having a sensing circuit for sensing cardiac activity (CA) for one or more cardiac cycles and generating one or more CA signals. An implantable pressure sensor (IPS) includes IPS sensing circuit for sensing pressure during the one or more cardiac cycles and generating one or more pressure signals. IMD and IPS include communications circuits for communicating with each other and/or an external device. One or both of IMD or IPS includes memory for storing program instructions and processor(s) for analyzing one of the CA or pressure signals, for one or more cardiac cycles, to detect a candidate arrhythmia. In response to detecting candidate arrhythmia, the processor(s) obtain another one of CA or pressure signals for cardiac cycles corresponding to the one or more cardiac cycles, and confirm or deny candidate arrhythmia based on the other one of the signals.

System and method for inter-device arrhythmia detection and confirmation

Described herein are methods, devices and systems for determining whether an R-wave detection should be classified as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO). One such method includes comparing a specific morphological characteristic (e.g., peak amplitude) associated with the R-wave detection to the specific morphological characteristic associated with each R-wave detection in a first set of earlier detected R-wave detections to thereby determine whether first TWO or PWO morphological criteria are met, and in a second set of earlier detected R-wave detections to thereby determine whether second TWO or PWO morphological criteria are met, wherein the second set differs from the first set but may have some overlap with the first set.

Methods and systems for determining whether R-wave detections should be classified as false due to T-wave oversensing (TWO) or P-wave oversensing (PWO)

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

Systems and methods are provided for detecting arrhythmias in cardiac activity is provided. The systems and methods include measuring conduction delays between an atria (A) and multiple left ventricular (LV) electrodes to obtain multiple intrinsic A/LV intervals, measuring conduction delays between a right ventricular (RV) and the multiple LV electrodes to obtain multiple intrinsic VV intervals. The systems and methods include calculating a first atrial ventricular (AV) delay based on at least one of the intrinsic A/LV intervals, and calculating a second AV delay based on at least one of the intrinsic VV intervals. The systems and methods include selecting a biventricular (BiV) pacing mode or an LV only pacing mode based on a relation between the first and second AV delays, and delivering a pacing therapy based on the selecting operation.

Method and system for biventricular or left ventricular pacing

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

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.

Described herein are methods, devices, and systems for identifying false R-R intervals, and false arrhythmia detections, resulting from R-wave undersensing or intermittent AV conduction block. Each of one or more of the R-R intervals is classified as being a false R-R interval in response to a duration the R-R interval being greater than a first specific threshold, and the duration the R-R interval being within a second specified threshold of being an integer multiple of at least X other R-R intervals for which information is obtained, wherein the integer multiple is at least 2, and wherein X is a specified integer that is 1 or greater. When performed for R-R intervals in a window leading up to a detection of a potential arrhythmic episode, results of the classifying can be used to determine whether the potential arrhythmic episode was a false positive detection.

Methods, devices and systems for identifying false R-R intervals and false arrhythmia detections due to R-wave undersensing or intermittent AV conduction block

Described herein are methods, devices, and systems that improve arrhythmia episode detection specificity, such as, but not limited to, atrial fibrillation (AF) episode detection specificity. Such a method can include obtaining an ordered list of R-R intervals within a window leading up to a detection of a potential arrhythmia episode, determining a measure of a dominant repeated R-R interval pattern within the window, and comparing the measure of the dominant repeated R-R interval pattern to a pattern threshold. If the measure of the dominant repeated R-R interval pattern is below the pattern threshold, that is indicative of a regularly irregular pattern being present, and there is a determination that the detection of the potential arrhythmia episode does not correspond to an actual arrhythmia episode. Such embodiments can beneficially be used to significantly reduce the number of false positive arrhythmia detections.

R-R interval pattern recognition for use in arrhythmia discrimination

Described herein are methods, devices, and systems that monitor heart rate and/or for arrhythmic episodes based on sensed intervals that can include true R-R intervals as well as over-sensed R-R intervals. True R-R intervals are initially identified from an ordered list of the sensed intervals by comparing individual sensed intervals to a sum of an immediately preceding two intervals, and/or an immediately following two intervals. True R-R intervals are also identified by comparing sensed intervals to a mean or median of durations of sensed intervals already identified as true R-R intervals. Individual intervals in a remaining ordered list of sensed intervals (from which true R-R intervals have been removed) are classified as either a short interval or a long interval, and over-sensed R-R intervals are identified based on the results thereof. Such embodiments can be used, e.g., to reduce the reporting of and/or inappropriate responses to false positive tachycardia detections.

Methods, devices and systems for distinguishing over-sensed R-R intervals from true R-R intervals

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

A computer implemented method for determining heart arrhythmias based on cardiac activity that includes under control of one or more processors of an implantable medical device (IMD) configured with specific executable instructions to obtain far field cardiac activity (CA) signals at electrodes located remote from the heart, and obtain acceleration signatures, at an accelerometer of the IMD, indicative of heart sounds generated during the cardiac beats. The IMD is also configured with specific executable instructions to declare a candidate arrhythmia based on a characteristic of at least one R-R interval from the cardiac beats, and evaluate the acceleration signatures for ventricular events (VEs) to re-assess a presence or absence of at least one R-wave from the cardiac beats and based thereon confirming or denying the candidate arrhythmia.

Implantable medical device for arrhythmia detection

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

Methods, systems, and devices for detecting sleep and apnea events

Computer implemented methods, devices and systems for monitoring a trend in heart failure (HF) progression are provided. The method comprises sensing left ventricular (LV) activation events at multiple LV sensing sites along a multi-electrode LV lead. The activation events are generated in response to an intrinsic or paced ventricular event. The method implements program instructions on one or more processors for automatically determining a conduction pattern (CP) across the LV sensing sites based on the LV activation events, identifying morphologies (MP) for cardiac signals associated with the LV activation events and repeating the sensing, determining and identifying operations, at select intervals, to build a CP collection and an MP collection. The method calculates an HF trend based on the CP collection and MP collection and classifies a patient condition based on the HF trend to form an HF assessment.

Heart failure progression monitoring based on LV conduction pattern and morphology trends

A computer implemented method for detecting arrhythmias in cardiac activity including obtaining far field cardiac activity (CA) signals for a series of beats. For at least a portion of the beats, the one or more processors perform, on a beat by beat basis: a) identifying first and second feature of interests (FOI) from a segment of the CA signal that corresponds to a current beat; and b) classifying the current beat into one of first and second groups. The method also includes designating one of the first and second groups to be a primary group based on a relation between the first and second groups, and for the beats in the primary group, selecting one of the first and second FOIs as the R-wave FOI. The method also includes rejecting an arrhythmia detection based on the P-waves detected.

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

Method and system to detect noise in cardiac arrhythmic patterns

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Valencia, CA, United States of America

Jong Gill