Experts has a new look! Let us know what you think of the updates.

Provide feedback
Home
Scholarly Works
Detecting Cardiac Abnormalities with Multi-Lead...
Conference

Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach

Abstract

Globally, heart disease has been the leading cause of death for more than two decades. There is a need to develop intelligent architectures to handle a variety of real life clinical scenarios when a 12-lead ECG is not a viable option. We propose a method using wide and deep CNN architectures to classify cardiac abnormalities from 12, 6, 4, 3, and 2 leads ECGs. These five networks were created for the PhysioNet/CinC Challenge 2021, by the …

Authors

Clark R; Heydarian M; Siddiqui K; Rashidiani S; Khan A; Doyle TE

Volume

48

Pagination

pp. 1-4

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

January 15, 2021

DOI

10.23919/cinc53138.2021.9662677

Name of conference

2021 Computing in Cardiology (CinC)