Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning-Reference-Cited by-同舟云学术

Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning

Published:2022-09-27 Issue: Volume: Page:
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Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Phan Thinh¹,Le Duc²,Brijesh Patel³,Adjeroh Donald⁴,Wu Jingxian⁵,Jensen Morten Olgaard⁶,Le Ngan²

Affiliation:

1. Chonnam National University,Department of AI Convergence,Gwangju,South Korea,61186

2. University of Arkansas,Department of CSCE,Fayetteville,Arkansas,72703

3. West Virginia University,Department of Cardiology,Morgantown,WV,26506

4. West Virginia University,Department of CSEE,Morgantown,WV,26506

5. University of Arkansas,Department of ELEG,Fayetteville,Arkansas,72703

6. University of Arkansas,Department of Biomedical Engineering,Fayetteville,Arkansas,72703

Publisher

IEEE

Link

Reference38 articles.

1. Diagnostic of multiple cardiac disorders from 12-lead ecgs using graph convolutional net based multi-label classification;jiang;2020 CinC,2020

4. Madnn: A multi-scale attention deep neural net for arrhythmia classification;duan;2020 CinC,2020

5. A topology informed random forest classifier for ecg classification;ignacio;2020 CinC,2020

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