Mayo Foundation for Medical Education and Research

Aegis Medical Innovations Inc.

Other

Hewlett-packard Development Company, L.p.

University of Minnesota

Anumana, Inc.

St. Anne's University Hospital Brno

Cardiothrive, Inc.

Paul A Friedman

Samuel J Asirvatham

Charles J Bruce

Itzhak Zachi Attia

Virend K Somers

Kevin E Bennet

Francisco Lopez-Jimenez

Suraj Kapa

Elliot Y K Hong

Trevor A McCaw

Peter A Noseworthy

David R Holmes, Iii

Christopher V DeSimone

Peter M Pollak

Sudhir Kushwaha

Samir Awasthi

Michael J Ackerman

Steven J Simske

Murali Aravamudan

Rakesh Barve

Tong Zhang

Douglas M Raymond

Daniel Robert Blakley

Venkataramanan Soundararajan

Walter T Savage

Shelley J Savage

Peter D Gray

Dorothy J Ladewig

Miroslav Novak

Pavel Leinveber

Zdenek Starek

Jiri Wolf

Soon J Park

Kyle D Traynor

Douglas S Beinborn

John J Dillon

Faisal F Syed

Tomas Kara

Bryan Lester Striemer

Ashim Prasad

Tyler Wagner

Jeremiah K Johnson

Michael J Levy

Ryan A Amara

Bo Qiang

Navtej S Buttar

Vaclav Kremen

Louis-Michel Wong Kee Song

Alena Talkachova

Elizabeth Rajan

Peter D Kozel

Katherine Carlson

Corinne Carpenter

Shivaram Poigai Arunachalam

Gianrico Farrugia

Juliane Bingener-Casey

Yash Gupta

Eunjung Lee

Jae K Oh

Alan M Sugrue

Siva K Mulpuru

Jennifer L Dugan

Bruce L Rowley

Hilary M Dubrock

Joerg Herrmann

John Rincón-Hekking

Sudhir S Kushwaha

An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.

Method and an apparatus for detecting a level of cardiovascular disease

Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

Noninvasive methods for detection of pulmonary hypertension

An apparatus and method for training an artificial intelligence-supported diagnostic assessment tool may provide rapid and accurate prognosis determinations. Apparatus may include at least a processor configured to receive a plurality of multi-channel sensor readings of physiological data, generate training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels, train a neural network using the plurality of diagnostic labels, receive a time series input describing user physiological data from at least a sensor, input the time series input into the trained neural network, generate diagnostic data as a function of the time series input and the trained neural network, determine prognostic data as a function of the diagnostic data, and output the prognostic data.

Apparatus and method for training an artificial intelligence-supported diagnostic assessment tool

This document describes methods and materials for the treatment of pathological conditions, including arrhythmias and trauma, using temperature modulation via implantable devices. For example, this document relates to methods and devices for treating atrial and/or ventricular fibrillation by cooling the epicardium.

Systems and methods for implantable devices

A system obtains a maternal electrocardiogram (ECG) signal that represents an ECG of a pregnant mother during a first time interval. The system further obtains a mixed maternal-fetal ECG signal that represents a combined ECG of the mother and her fetus during the first time interval; processes the maternal ECG signal and the mixed maternal-fetal ECG signal to generate a fetal ECG signal that represents the ECG of the fetus during the time interval, the fetal ECG signal substantially excluding the maternal ECG signal; and provides an output based on the fetal ECG signal.

Systems and methods for fetal monitoring

This document describes methods and devices for securing epicardial devices. For example, this document describes methods and devices for securing epicardial devices by passing a sheath into a pericardial space of a patient through a first percutaneous access site, passing a guidewire through the sheath into the pericardial space of the patient, passing the guidewire through a transverse sinus of the patient, passing a snare device into the pericardial space through a second percutaneous access site, and capturing a free end portion of the guidewire using the snare device.

Methods and devices for securing epicardial devices

Devices and methods are provided for the treatment of pathological conditions including arrhythmias and trauma using temperature modulation via implantable or worn devices. For example, in one example embodiment this document relates to devices and methods for treating atrial or ventricular fibrillation by cooling the epicardium. The devices and methods can also be used to treat other disorders by applying heating and/or cooling to a patient in a number of different manners.

Thermal modification to treat cardiac electrical disorders and other disorders

Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.

ECG-based cardiac ejection-fraction screening

Devices and methods for the treatment of heart conditions, and other medical purposes, include cardiac pacing systems. For example, this document describes temporary transvenous endocardial pacemaker systems and devices for deploying such systems without the need for conventional imaging procedures.

Temporary pacemaker systems and deployment systems

Provided herein are methods, systems, and computer program products for the detection and evaluation of cardiac condition in a lead-invariant manner.

Machine-learning for processing lead-invariant electrocardiogram inputs

Systems, methods, devices, and techniques for estimating an ejection-fraction characteristic of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to an ejection-fraction predictive model, such as a neural network or other machine-learning model. In response, the ejection-fraction predictive model processes the input to generate an estimated ejection-fraction characteristic of the mammal. The system outputs the estimated ejection-fraction characteristic of the mammal for presentation to a user.

This document provides devices and methods for monitoring health patient parameters using a wearable monitoring device. For example, this document provides monitoring devices with multiple sensors, multiple types of sensors, algorithms for managing the multiple sensors to enhance monitor performance, and for detection of health events using health parameter data that is acquired by the monitoring system.

Sensor types and sensor positioning for a remote patient monitoring system

Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.

Neural networks for atrial fibrillation screening

Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.

Predicting transient ischemic events using ECG data

Growing community of inventors

Rochester, MN, United States of America

Paul A Friedman