High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning.

Journal: JAMA cardiology
Published Date: Jan 1, 2020

Abstract

This study uses video and a pretrained deep convolutional neural network to analyze facial photoplethysmographic signals in detection of atrial fibrillation.

Authors

  • Bryan P Yan
    Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • William H S Lai
    Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Christy K Y Chan
    Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Alex C K Au
    Division of Cardiology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong SAR, China.
  • Ben Freedman
    Heart Research Institute, Charles Perkins Centre, and Concord Hospital Cardiology, University of Sydney, Australia.
  • Yukkee C Poh
    Cardiio Inc, Cambridge, Massachusetts.
  • Ming-Zher Poh
    Cardiio, Cambridge, Massachusetts, USA.

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