Pacesetter, Inc.

Nima Badie

Luke C McSpadden

Jong Gill

Jan O Mangual-Soto

Fujian Qu

Kyungmoo Ryu

Wenwen Li

Aditya Goil

Louis-Philippe Richer

Gene A Bornzin

Stuart Rosenberg

Chunlan Jiang

Xing Pei

Caroline D Jordan

Brad Lindevig

Xiaoyi Min

Gabriel A Mouchawar

Yelena Nabutovsky

Benjamin T Persson

Fady Dawoud

Cyrille Casset

Didier Theret

Weiqun Yang

Jennifer Rhude

Yun Qiao

Avi Fischer

Jan Mangual

Carin Folman

Donald Lee Hopper

David Ligon

Mostafa Sadeghi

Leyla Sabet

Kevin J Davis

Chaoyi Kang

Christopher Gloschat

Luke A McSpadden

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)

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

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

Described herein are methods for use with an implantable system including at least an atrial leadless pacemaker (aLP). Also described herein are specific implementations of an aLP, as well as implantable systems including an aLP. In certain embodiments, the aLP senses a signal from which cardiac activity associated with a ventricular chamber can be detected by the aLP itself based on feature(s) of the sensed signal. The aLP monitors the sensed signal for an intrinsic or paced ventricular activation within a ventricular event monitor window. In response to the aLP detecting an intrinsic or paced ventricular activation itself from the sensed signal within the ventricular event monitor window, the aLP resets an atrial escape interval timer that is used by the aLP to time delivery of an atrial pacing pulse if an intrinsic atrial activation is not detected within an atrial escape interval.

Ventricular far-field sensing to guide atrial leadless pacemaker and beyond

Embodiments describe herein generally pertain to implantable medical device (IMDs), and methods for use therewith, that can be used to automatically switch an IMD from its normal operational mode to magnetic resonance imaging (MRI) safe mode, and vice versa, within increased specificity. A controller of an IMD is configured to use an accelerometer to determine whether a positional condition associated with a patient is detected, and control sampling of a magnetic field sensor or at least one signal output therefrom, such that a first sampling rate is used when the positional condition is detected, and a second sampling rate, that is slower than the first sampling rate, is used when the positional condition is not detected, to thereby conserve power. Based on results of the sampling, the controller determines whether a magnetic field condition is detected, and in response thereto performs a mode switch to an MRI safe mode.

Implantable medical devices, and methods of use therewith, that detect MRI systems with enhanced specificity

A system and method are provided for managing atrial-ventricular (AV) delay adjustments. An AV interval is measured that corresponds to an interval between an atrial paced (Ap) event or an atrial sensed (As) event and a sensed ventricular (Vs) event. A candidate AV delay is set based on the AV interval and a bundle branch adjustment (BBA) value. A QRS characteristic of interest (COI) is measured while utilizing the candidate AV delay in connection with delivering a pacing therapy. The BBA value is adjusted and the candidate AV delay is reset based on the BBA value as adjusted. A collection of QRS COIs and corresponding candidate AV delays are obtained and one of the candidate AV delays is selected as a BBA AV delay. The pacing therapy is managed, based on the BBA AV delay.

Systems and methods for managing atrial-ventricular delay adjustments

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

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

A system and method have at least one implantable lead comprising a right ventricular (RV) electrode and one or more left ventricular (LV) electrodes, at least one processor, and a memory coupled to the at least one processor. The memory stores program instructions. The program instructions are executable by the at least one processor to determine a right ventricular to left ventricular (RV-LV) conduction time representative of a conduction time between a right ventricular (RV) paced event and one or more responsive left ventricular (LV) sensed events, determine a left ventricular to right ventricular (LV-RV) conduction time representative of a conduction time between one or more LV paced event and an RV sensed events, calculate a relation between the RV-LV conduction time and the LV-RV conduction time, and set a pacing mode of an implantable medical device to one of i) a biventricular (BiV) pacing mode and ii) an LV only pacing mode based on the relation between the RV-LV conduction time and the LV-RV conduction time.

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

Disclosed herein is a catheter for delivering an implantable medical lead to an implantation site near an ostium leading to a proximal region of a coronary sinus. The catheter includes a distal end, a proximal end opposite the distal end, a tubular body extending between the distal and proximal ends, an atraumatic fixation structure defining a distal termination of the distal end, and a lead receiving lumen. The atraumatic fixation structure is configured to enter the ostium and passively pivotally anchor with the proximal region of the coronary sinus. The lead receiving lumen extends along the tubular body from the proximal end to an opening defined in a side of the tubular body near the distal end and proximal the atraumatic fixation structure.

Coronary sinus-anchored sheath for delivery of his bundle pacing lead

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.

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Berkeley, CA, United States of America

Nima Badie