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

Soonil Kwon 1*, MD;  Joonki Hong 2*, MS;  Eue-Keun Choi 1, MD, PhD;  Euijae Lee 1, MD;  David Earl Hostallero 2, BS;  Wan Ju Kang 2, BS;  Byunghwan Lee 3, MS;  Eui-Rim Jeong 4, PhD;  Bon-Kwon Koo 1, MD, PhD;  Seil Oh 1, MD, PhD, FHRS, FESC;  Yung Yi 2, PhD

1 Department of Internal Medicine, Seoul National University Hospital , Seoul , KR

2 School of Electrical Engineering, KAIST , Daejeon , KR

3 Sky Labs Inc , Seongnam , KR

4 Department of Information and Communication Engineering, Hanbat National University , Daejeon , KR

*these authors contributed equally

Corresponding Author:

  • Eue-Keun Choi, MD, PhD
  • Department of Internal Medicine
  • Seoul National University Hospital
  • 101 Daehang-ro, Jongno-gu
  • Seoul
  • KR
  • Phone: 82 2-2072-0688
  • Email: choiek417@gmail.com