Pacesetter, Inc.

St. Jude Medical, Cardiology Division, Inc.

Fujian Qu

Stuart Rosenberg

Jong Gill

Gene A Bornzin

Yelena Nabutovsky

Fady Dawoud

Nima Badie

Neha Malhotra

Edward Karst

Kyungmoo Ryu

Taraneh Ghaffari Farazi

Riddhi Shah

Xiaoyi Min

Hoda Razavi

Luke C McSpadden

Alex C Soriano

Dan E Gutfinger

Ryan Rooke

Michael E Benser

Xing Pei

Rupinder Bharmi

Wenwen Li

Lisa P Weinberg

Aditya Goil

Caroline D Jordan

Timothy Pflugh

Benjamin Coppola

Steve Koh

Donald Lee Hopper

Wenbo Hou

Timothy A Fayram

Yongjian Wu

Allen J Keel

Cyrille Casset

Alan B Vogel

Chunlan Jiang

Didier Theret

Yun Qiao

Edith Merle Arnold

Mark Carlson

Rohan More

Julie Prillinger

Pritika Toutam

Stephanie C Sun

Craig A Hewitt

Kwangdeok Lee

Sinny Delacroix

Euljoon Park

Brian Jeffrey Wenzel

Andreas Blomqvist

Jennifer Rhude

Cem M Shaquer

Lalit Venkatesan

Mostafa Sadeghi

Carin Folman

Kevin J Davis

Leyla Sabet

Christopher Gloschat

Craig Markovitz

Elnaz Lashgari

Stuart Rosenbeg

Philipp Sommer

Sergio Richter

Daniel F Booth

Mark J Sprowls

Luke 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)

A system is provided that includes one or more processors, and a memory coupled to the one or more processors. The memory stores program instructions, and the program instructions are executable by the one or more processors. When executed, the one or more processors obtain cardiac activity (CA) signals for a series of beats, and identify whether a characteristic of interest (COI) from a first segment of the CA signals exceeds a COI limit. The one or more processors also analyze morphology of the CA signals for the series of beats responsive to the first segment of the CA signals exceeding the COI limit, and based on the analyze operation, identify a premature ventricular contraction (PVC) within the series of beats.

System for identifying premature ventricular contractions

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

Computer implemented methods and systems are provided that comprise, under control of one or more processors of a medical device, where the one or more processors are configured with specific executable instructions. The methods and systems obtain motion data indicative of at least one of a posture or a respiration cycle; obtain cardiac activity (CA) signals for a series of beats; identify whether a characteristic of interest (COI) from at least a first segment of the CA signals exceeds a COI limit; analyze the motion data to determine whether at least one of the posture or respiration cycle at least in part caused the COI to exceed the COI limit. Based on the analyzing operation, the methods and systems automatically adjust a CA sensing parameter utilized by the medical device to detect R-waves in subsequent CA signals; and detect an arrhythmia based on a presence or absence of one or more of the R-waves in at least a second segment of the CA signals.

Methods and systems for reducing false declarations of arrythmias

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

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 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

System and methods are provided for determining a stimulation threshold for closed loop spinal cord stimulation (SCS). The system and methods provide a lead coupled to an implantable pulse generator (IPG). The system and methods deliver SCS pulses from the IPG to the lead electrodes in accordance with an SCS therapy and determine an evoked compound action potential (ECAP) amplitude based on an ECAP waveform resulting from the SCS therapy. The system and methods increase the SCS therapy by increasing at least one of an amplitude, a duration, and number of the SCS pulses associated with the SCS therapy. The system and methods also include iteratively repeat the delivering, determining and increasing operations until the ECAP amplitude exhibits a downward trend divergence. The system and methods define a stimulation threshold based on the ECAP amplitude at the trend divergence.

System and method for determining a stimulation threshold for closed loop spinal cord stimulation

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

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.

Described herein are methods, devices, and systems for improving R-wave detection sensitivity and positive predictive value, and for improving arrhythmia detection accuracy. Certain embodiments involve determining whether to classify a potential R-wave as a false R-wave (or more specifically, an over-sensed P-wave) by determining a measure of magnitude of a first portion of the signal corresponding to a first window following the potential R-wave, determining the measure of magnitude of a second portion of the signal corresponding to a second window following the first window, and classifying the potential R-wave as a false R-wave if the measure of magnitude of the second portion of the signal is at least a specified extent larger (e.g., at least 3 times larger) than the measure of magnitude of the first portion of the signal. Certain embodiments also involve adjusting an R-wave marker for a potential R-wave that is classified as a false R-wave.

Methods, devices and systems for improving R-wave detection and arrhtymia detection accuracy

Computer implemented methods, systems and devices are provided to monitor for potential heart failure (HF). Cardiac activity (CA) data is obtained and filtered to obtain respiration data indicative of a respiration pattern. The respiration data is analyzed to determine one or more respiration characteristics of interest (COI) that are recorded along with collection time information to form an HF monitoring log. Additionally or alternatively, the CA data is analyzed to detect an event of interest. The cardiac activity data is filtered to obtain respiration data indicative of a respiration pattern, and the respiration data is analyzed for respiration induced under detection of the event of interest from the CA data.

Heart failure monitoring and reduction of respiration induced under sensing of cardiac events

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San Jose, CA, United States of America

Fujian Qu