Cardiac Pacemakers, Inc.

Boston Scientific Scimed, Inc.

Boston Scientific Neuromodulation Corporation

Cameron Health, Inc.

Cardiac Pacamakers, Inc.

Pramodsingh Hirasingh Thakur

Qi An

Viktoria A Averina

Barun Maskara

Yi Zhang

Shibaji Shome

Keith R Maile

Yinghong Yu

Bryan Allen Clark

Julie A Thompson

David J Ternes

Allan Charles Shuros

Jeffrey E Stahmann

Sunipa Saha

Jianwen Gu

Ramesh Wariar

Bin Mi

Kyle Harish Srivastava

Elizabeth Mary Annoni

Michael John Kane

Shantha Arcot-Krishnamurthy

Krzysztof Z Siejko

David L Perschbacher

John D Hatlestad

Stephen B Ruble

Abhilash Patangay

Jacob I Laughner

Jonathan Bennett Shute

James John Kleinedler

Robert J Sweeney

Rezwan Ahmed

Deepa Mahajan

Scott A Meyer

Kevin John Stalsberg

Paul Hultz

Brendan Early Koop

Rosana Esteller

Stephen John Hahn

Craig Michael Stolen

Amy Jean Brisben

Kenneth C Beck

Howard D Simms, Jr

Gezheng Wen

Carlos Alberto Ricci

Vladimir V Kovtun

William Conrad Stoffregen

Jonathan Walter Krueger

Kent Lee

Michael X Govea

Sandra Nagale

Lynne E Swanson

Zhe Shen

Dat Thanh Huynh

James O Gilkerson

G Shantanu Reddy

Brian D Soltis

Ron A Balczewski

Natalie A Brill

Eric Falbe Hammill

Paul F Emerson

Dennis Werner

Timothy Edward Meyer

Jason Humphrey

Maya Bhat

Jiang Ding

Dan Li

Aaron R McCabe

Jason J Hamann

Bradley Lawrence Hershey

Lili Liu

Keith L Herrmann

David Blum

Mary M Byron

Arjun D Sharma

Bruce R Forsyth

Michael James Dufresne

Juan G Hincapie

JoAnna Trapp Simpson

Kyle H Srivastava

Stevens L Martin

Yan Zou

Ashley Moriah Jensen

Melanie Anne Zile

Jianjun Yuan

Amy Jean Brisden

Systems and methods are disclosed to determine a measure of patient weight using existing medical device sensors, comprising receiving acceleration information of a patient and, if a value of the acceleration information exceeds an activity threshold over a measurement window, detecting patient steps in the measurement window using the acceleration information, determining a patient step rate over the measurement window using the detected patient steps, determining a measure of patient step force for the measurement window, and determining a measure of patient weight using the determined patient step rate and measure of patient step force.

Acceleration based patient weight determination

Systems and methods for assessing a cardiac arrhythmia risk of a patient, such as a risk for developing atrial fibrillation, are disclosed. An exemplary medical-device system includes a risk stratifier circuit configured to, in an absence of prior and present atrial arrhythmia, determine a composite risk of the patient developing a future atrial arrhythmia using a trained machine-learning model and a plurality of features of physiological information sensed from the patient and an arrhythmia monitor circuit configured to adjust an arrhythmia monitoring parameter based at least in part on the composite risk and to detect an atrial arrhythmia event using the adjusted arrhythmia monitoring parameter.

Systems and methods for predicting atrial arrhythmia

Systems and methods for monitoring patients with multiple chronic diseases are described. A system may include a health status monitor that receives diagnostic data including physiological signals sensed from a patient. The system may produce at least a first risk indication of the patient developing a first disease and a second risk indication of the patient developing a different second disease. The system may detect the first and second diseases from the physiological signals, and generate a composite health status indicator using the detections of the first and second diseases and the first and second risk indications. An alert of worsening health status may be generated if the composite detection score exceeds an alert threshold.

Multi-disease patient management

Systems and methods for monitoring heart failure (HF) status in a patient are discussed. An exemplary system includes a gait analyzer circuit than can receive gait or balance information, and generate a gait feature such as gait speed or a gait pattern. The system includes a HF detector circuit that can detect patient HF status, or to predict patient risk of a future worsening heart failure (WHF) event, using the gait feature. In some examples, the system may trigger sensing physiologic information according to the detected gait, and detect patient HF status using the sensed physiologic information. The system can initiate or adjust a heart failure therapy according to the HF status or the WHF risk.

Heart failure monitor using gait information

Embodiments of the present disclosure relate to imaging a body part using sounds. In embodiments, a system comprises a motion sensor and a processing device communicatively coupled to the motion sensor. The motion sensor is configured to sense an acceleration wave produced by a sound emitted by a source and generate acceleration measurements in response to sensing the acceleration wave, wherein the source is associated with the body part of a subject. The processing device is configured to receive the acceleration measurements and determine a location of the source using a location of the motion sensor and the acceleration measurements. In addition, the processing device is configured to image the body part of the subject using the determined location of the source and the acceleration measurements.

Imaging of a body part using sounds

This document discusses, among other things, systems and methods for managing pain in a patient. A system may include sensors to sense physiological or functional signals, and a pain analyzer that generates a pain score using the sensed physiological or functional signals and a fusion model. The system includes a calibration module that calibrates the fusion model based on measurements from the sensed physiological or functional signals and a reference pain quantification corresponding to multiple pain intensities. A pain score may be generated using the calibrated fusion model. The system can additionally include a neurostimulator that controls the delivery of pain therapy by adjusting one or more stimulation parameters based on the pain score.

Patient-specific calibration of pain quantification

This document discusses, among other things, systems and methods to receive physiologic information from a patient and to generate a heart failure status using the received physiologic information. Based on the received physiologic information, the generated heart failure status can be classified into at least one of a set of predetermined phenotype clusters.

Phenotyping sensor patterns to identify and treat heart failure

This document discusses, among other things, systems and methods for managing pain in a subject. A system may include one or more sensors configured to sense from the subject information corresponding to emotional reaction to pain, such as emotional expression. The emotional expression includes facial or vocal expression. A pain analyzer circuit may generate a pain score using signal metrics of facial or vocal expression extracted from the sensed information. The pain score may be output to a user or a process. The system may additionally include a neurostimulator that can adaptively control the delivery of pain therapy by automatically adjusting stimulation parameters based on the pain score.

Pain management based on emotional expression measurements

Systems and methods are described for subject rehospitalization management. In an example, multiple physiologic signals can be obtained from a subject using multiple sensors. In response to a hospitalization event, pre-hospitalization characteristics of the multiple physiologic signals can be identified. Post-hospitalization characteristics of the multiple physiologic signals can be identified, including characteristics that differ from their corresponding pre-hospitalization characteristics. Later subsequent physiologic signals can be further monitored after the hospitalization event, such as using the same multiple sensors, and subsequent physiologic signal characteristics can be identified. In an example, a heart failure diagnostic indication can be determined using information about the pre-hospitalization characteristics, the post-hospitalization characteristics, and the subsequent characteristics. Information about relative changes in signal characteristics from multiple sensors can be used to identify particular subject physiologic signals to monitor during subsequent periods.

Heart failure management to avoid rehospitalization

Systems and methods for monitoring patients with a chronic disease such as heart failure are disclosed. The system may include a physiological sensor circuit to sense physiological signals and generate signal metrics from the physiological signals. The system may include a health status analyzer circuit to use the signal metrics to generate one or more stability indicators of patient health status, such as stability of heart failure status. The system may additionally generate one or more health status indicators indicating patient health status such as heart failure progression. A patient disposition decision may be generated using the health status indicators and the stability indicators to provide an indication of readiness for patient discharge from or a risk of admission to a hospital.

Systems and methods for patient monitoring

This document discusses, among other things, systems and methods to determine an indication of contractility of a heart of a patient using received physiologic information, and to determine blood pressure information of the patient using the heart sound information and the determined indication of contractility of the heart. The system can include an assessment circuit configured to determine an indication of contractility of a heart of the patient using first heart sound (S) information of the patient, and to determine blood pressure information of the patient using second heart sound (S) information of the patient and the determined indication of contractility of the heart.

Chronic monitoring of blood pressure using heart sounds

Systems and methods for monitoring patients with a chronic disease are described. A patient management system may sense physiological signals from a patient using one or more implantable or other ambulatory sensors, and generate from the physiological signals a chronobiological rhythm indicator (CRI) such as indicating a circadian rhythm. A reference CRI associated with a prior hospital admission event of the patient may be provided to the patient management system, which compares the CRI to the reference CRI and generates a readmission risk score indicating the patient's risk of subsequent hospital readmission due to a worsened condition of the chronic disease. The readmission risk score may be provided to a user or a process, or used to initiate or adjust a therapy delivered to the patient.

Readmission risk assessment based on chronobiological rhythms

Systems and methods to are disclosed to determine a sleep disordered breathing parameter of a patient, including receiving respiration information of the patient and temperature information of the patient and to determine the sleep disordered breathing parameter of the patient using the received respiration information and temperature information of the patient.

Temperature monitoring for sleep disordered breathing

Systems and methods to provide remote patient monitoring for viral-respiratory symptoms, including coronavirus or COVID-19 symptoms are disclosed, including a signal receiver circuit configured to receive first and second physiologic information of a patient, the first physiologic information comprising respiration rate information of a patient and the second physiologic information different than the first physiologic information, and an assessment circuit configured to determine an indication of patient viral-respiratory disease using the received first and second physiologic information.

COVID-19 remote monitoring

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes a first sensor circuit to sense a first signal indicative of a functional state of the subject, a second sensor circuit to sense a second signal different from the first signal, and a controller circuit. The controller circuit may determine an operating mode of the second sensor circuit according to the sensed first signal, trigger the second senor circuit to sense the second signal under the determined operating mode, and generate a pain score using at least the second signal sensed under the determined operating mode. The pain score may be output to a patient or used for closed-loop control of a pain therapy.

Sensor-based pain management systems and methods

Growing community of inventors

Woodbury, MN, United States of America

Pramodsingh Hirasingh Thakur