Mayo Foundation for Medical Education and Research

Peter A Noseworthy

Paul A Friedman

Itzhak Zachi Attia

Samuel J Asirvatham

Suraj Kapa

Francisco Lopez-Jimenez

Rakesh Barve

Charles J Bruce

Kevin E Bennet

Ashim Prasad

Bryan Lester Striemer

Christopher V DeSimone

Virend K Somers

Samir Awasthi

Bo Qiang

Michael J Ackerman

Vaclav Kremen

Yash Gupta

John J Dillon

Joerg Herrmann

John Rincón-Hekking

Alan M Sugrue

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

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

Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.

Electrocardiogram analytical tool

Growing community of inventors

Rochester, MN, United States of America

Peter A Noseworthy