Cardiac Pacemakers, Inc.

Cyberonics, Inc.

Livanova USA, Inc.

Gerrard Merrill Carlson

William C Lincoln

Jeffrey E Stahmann

Imad Libbus

Keith R Maile

Krzysztof Z Siejko

Ramesh Wariar

Marina V Brockway

Julio Cesar Spinelli

Qingsheng Zhu

Abhilash Patangay

Yinghong Yu

Joseph M Pastore

Veerichetty A Kadhiresan

Donald Lee Hopper

Andrew P Kramer

Jiang Ding

Rodney W Salo

Carlos Haro

Jay A Warren

William Hsu

Laura Green

Yi Zhang

Avram Scheiner

Yayun Lin

Yousufali H Dalal

Haresh G Sachanandani

Allan Charles Shuros

John D Hatlestad

Robert J Sweeney

Kent Lee

Shantha Arcot-Krishnamurthy

Douglas R Daum

Kevin John Stalsberg

Shelley Marie Cazares

Geng Zhang

Yachuan Pu

Kenneth C Beck

Andres Belalcazar

Jon Peterson

John A Dyjach

Richard O Kuenzler

Bruce H KenKnight

Bruce Alan Tockman

Badri Amurthur

Anthony V Caparso

Ronald W Heil, Jr

Ronald David Berger

Mohan Krishnan

David M Flynn

Vladimir V Kovtun

John E Heil

Mary Lee Cole

Peter T Kelley

Loell Boyce Moon

Curtis Charles Lindstrom

Jeffrey T Bartig

Randall M Peterfeso

Carol Werlein

Aaron W Janke

Robert S Booker, Iii

Gary W Goebel

Douglas A Heitkamp

Dan Li

Stephen John Hahn

Kenneth L Baker

Shivkumar Sabesan

Bruce R Jones

Paul Craig Haldeman

Jan P Heemels

Bruce H Kenknight

Chie Kawahara

A system and method to sense heart sounds with one or more implantable medical devices according to one or more signal processing parameters. The method alters one or more of the parameters as a function of one or more physiologic triggering events. The method then senses heart sounds with the one or more implantable medical devices according to at least the one or more altered signal processing parameters.

Adaptive sampling of heart sounds

This document discusses, among other things, systems and methods to enable physician labels on a remote server and use labels to verify and improve algorithm results. A method comprises using patient data in an automated analysis to obtain a result; receiving a message from the user, wherein the message is related to the result; and using at least a portion of the message to automatically modify the analysis.

Method and system to enable physician labels on a remote server and use labels to verify and improve algorithm results

A cardiac rhythm management system provides for the trending of a third heart sound (S3) index. The S3 index is a ratio, or an estimate of the ratio, of the number of S3 beats to the number of all heart beats, where the S3 beats are each a heart beat during which an occurrence of S3 is detected. An implantable sensor such as an accelerometer or a microphone senses an acoustic signal indicative heart sounds including S3. An S3 detector detects occurrences of S3 from the acoustic signal. A heart sound processing system trends the S3 index on a periodic basis to allow continuous monitoring of the S3 activity level, which is indicative of conditions related to heart failure.

Third heart sound activity index for heart failure monitoring

A system and method for managing preload reserve and tracking the inotropic state of a patient's heart. The S1 heart sound is measured as a proxy for direct measurement of stroke volume. The S3 heart sound may be measured as a proxy for direct measurement of preload level. The S1-S3 pair yield a point on a Frank Starling type of curve, and reveal information regarding the patient's ventricular operating point and inotropic state. As an alternative, or in addition to, measurement of the S3 heart sound, the S4 heart sound may be measured or a direct pressure measurement may be made for the sake of determining the patient's preload level. The aforementioned measurements may be made by a cardiac rhythm management device, such as a pacemaker or implantable defibrillator.

Managing preload reserve by tracking the ventricular operating point with heart sounds

A system and method provide heart sound tracking, including an input circuit, configured to receive heart sound information, and a heart sound recognition circuit. The heart sound recognition circuit can be coupled to the input circuit and can be configured to recognize, within a particular heart sound of a particular heart sound waveform, a first intra heart sound energy indication and a corresponding first intra heart sound time indication using the heart sound information from the particular heart sound waveform and the heart sound information from at least one other heart sound waveform. The particular heart sound can include at least a portion of one of S1, S2, S3, and S4. Further, the first intra heart sound energy indication and the corresponding first intra heart sound time indication can correspond to the at least a portion of one of S1, S2, S3, and S4, respectively.

Heart sound tracking system and method

An implantable activity detector can detect metabolic stress levels, which can be normalized, such as to identify times of activities such as walking and running or to identify trends such as a decrease in metabolic activity. The data can be derived from different sources such as an accelerometer and pedometer. This data can be compared to independently specifiable thresholds, such as to trigger an alert or responsive therapy, or to display one or more trends. The information can also be combined with other congestive heart failure (CHF) indications. The alert can notify the patient or a caregiver, such as via remote monitoring. Metabolic activity data from one or more of the activity detectors can be used to establish a model of metabolic stress, to which further activity data can be compared for identifying periods of increased or decreased metabolic stress.

Logging daily average metabolic activity using a motion sensor

This disclosure relates to devices, systems, and methods for validating locational data for a monitoring device. The external monitoring device located on a patient may include one or more processors, one or more memory devices, one or more power devices, one or more heart rate detection devices, and one or more heart sound detection devices. Further, the method may include determining a plurality of status of an external monitoring device located on a patient via one or more processors based on obtained heart rate data and obtained heart sound data. The external monitoring device state may be generated via a validation module based on the heart rate data and the heart sound data.

Systems and methods for validating monitoring device placement and locations

This document discusses, among other things, systems and methods for using one or more inter-relations between within-patient detection methods and between-patient stratifier methods. A method comprises obtaining results of a first physiological assessment, wherein the first physiological assessment includes a between-patient assessment; and using the results to adjust one or more parameters of a second physiological assessment, wherein the second physiological assessment includes a within-patient assessment.

Inter-relation between within-patient decompensation detection algorithm and between-patient stratifier to manage HF patients in a more efficient manner

Systems and methods provide for detecting respiration disturbances and changes in respiration disturbances, preferably by detecting variability in one or more respiration parameters. Respiration rate variability is determined for a variety of diagnostic and therapeutic purposes, including disease/disorder detection, diagnosis, treatment, and therapy titration. Systems and methods provide for generating a footprint, such as a two- or three-dimensional histogram, representative of a patient's respiration parameter variability, and generating one or more indices representative of quantitative measurements of the footprint.

Respiration monitoring using respiration rate variability

A system and method provide for systolic interval analysis. In an example, an implantable device measures a cardiac impedance signal. A transformation of the cardiac impedance interval is generated. The device also measures a heart sound signal. A time interval between a point on the transformed signal of the cardiac impedance signal and a point on the heart sound signal is calculated.

System and method for systolic interval analysis

A system and method for assessing cardiac performance through cardiac vibration monitoring is described. Cardiac vibration measures are directly collected through an implantable medical device. Cardiac events including at least one first heart sound reflected by the cardiac vibration measures are identified. The first heart sound is correlated to cardiac dimensional measures relative to performance of an intrathoracic pressure maneuver. The cardiac dimensional measures are grouped into at least one measures set corresponding to a temporal phase of the intrathoracic pressure maneuver. The at least one cardiac dimensional measures set is evaluated against a cardiac dimensional trend for the corresponding intrathoracic pressure maneuver temporal phase to represent cardiac performance.

System and method for assessing cardiac performance through cardiac vibration monitoring

An implantable device and method for monitoring S1 heart sounds with a remotely located accelerometer. The device includes a transducer that converts heart sounds into an electrical signal. A control circuit is coupled to the transducer. The control circuit is configured to receive the electrical signal, identify an S1 heart sound, and to convert the S1 heart sound into electrical information. The control circuit also generates morphological data from the electrical information. The morphological data relates to a hemodynamic metric, such as left ventricular contractility. A housing may enclose the control circuit. The housing defines a volume coextensive with an outer surface of the housing. The transducer is in or on the volume defined by the housing.

Sensing rate of change of pressure in the left ventricle with an implanted device

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Champlin, MN, United States of America

Gerrard Merrill Carlson