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

Journal: JMIR mHealth and uHealth
Published Date: Jun 6, 2019

Abstract

BACKGROUND: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability.

Authors

  • Soonil Kwon
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Joonki Hong
    School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
  • Eue-Keun Choi
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Euijae Lee
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • David Earl Hostallero
    School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
  • Wan Ju Kang
    School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
  • Byunghwan Lee
    Sky Labs Inc, Seongnam, Republic of Korea.
  • Eui-Rim Jeong
    Department of Information and Communication Engineering, Hanbat National University, Daejeon, Republic of Korea.
  • Bon-Kwon Koo
    Department of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Seil Oh
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Yung Yi
    School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.

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