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

Wearable Devices and Sensors (888) New Methods (944) Tools, Programs and Algorithms (266) Cardiac Arrhythmia, Atrial Fibrillation (84) Novel Sensors and Data Acquisition Methods in Cardiology (196)

Published on in Vol 7, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12770, first published .
Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Authors of this article:

Soonil Kwon1 Author Orcid Image ;   Joonki Hong2 Author Orcid Image ;   Eue-Keun Choi1 Author Orcid Image ;   Euijae Lee1 Author Orcid Image ;   David Earl Hostallero2 Author Orcid Image ;   Wan Ju Kang2 Author Orcid Image ;   Byunghwan Lee3 Author Orcid Image ;   Eui-Rim Jeong4 Author Orcid Image ;   Bon-Kwon Koo1 Author Orcid Image ;   Seil Oh1 Author Orcid Image ;   Yung Yi2 Author Orcid Image
Article Authors Cited by (64) Tweetations (2) Metrics

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Books/Policy Documents

  1. Mejía-Mejía E, Allen J, Budidha K, El-Hajj C, Kyriacou P, Charlton P. Photoplethysmography. View
  2. Chouvarda I. Personalized Health Systems for Cardiovascular Disease. View
  3. Chung C, Roy V, Tse G, Liu H. Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing. View