This paper is in the following e-collection/theme issue:

Machine Learning (1370) Digital Health Reviews (1174) Decision Support for Health Professionals (1127) Electronic Health Records (961)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38454, first published .
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Authors of this article:

Georgios Petmezas1 Author Orcid Image ;   Leandros Stefanopoulos1 Author Orcid Image ;   Vassilis Kilintzis1 Author Orcid Image ;   Andreas Tzavelis2 Author Orcid Image ;   John A Rogers3 Author Orcid Image ;   Aggelos K Katsaggelos4 Author Orcid Image ;   Nicos Maglaveras1 Author Orcid Image
Article Authors Cited by (19) Tweetations (4) Metrics

Georgios Petmezas   1 , BSc, MSc ;   Leandros Stefanopoulos   1 , BSc, MSc ;   Vassilis Kilintzis   1 , BSc, MSc, PhD ;   Andreas Tzavelis   2 , BSc ;   John A Rogers   3 , BSc, MSc, PhD ;   Aggelos K Katsaggelos   4 , PhD ;   Nicos Maglaveras   1 , MSc, PhD

1 Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece

2 Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States

3 Department of Material Science, Northwestern University, Evanston, IL, United States

4 Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States

Corresponding Author:

  • Nicos Maglaveras, MSc, PhD
  • Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies
  • The Medical School
  • Aristotle University of Thessaloniki
  • University Campus - Box 323
  • Thessaloniki, 54124
  • Greece
  • Phone: 30 2310999281
  • Email: nicmag@auth.gr