Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

Journal: Heart (British Cardiac Society)
PMID: 29853485

Abstract

OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.

Authors

  • Ming-Zher Poh
    Cardiio, Cambridge, Massachusetts, USA.
  • Yukkee Cheung Poh
    Cardiio, Cambridge, Massachusetts, USA.
  • Pak-Hei Chan
    Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong.
  • Chun-Ka Wong
    Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR.
  • Louise Pun
    Department of Family Medicine and Primary Healthcare, Hong Kong East Cluster, Hospital Authority, Hong Kong.
  • Wangie Wan-Chiu Leung
    Department of Family Medicine and Primary Healthcare, Hong Kong East Cluster, Hospital Authority, Hong Kong.
  • Yu-Fai Wong
    Department of Family Medicine and Primary Healthcare, Hong Kong East Cluster, Hospital Authority, Hong Kong.
  • Michelle Man-Ying Wong
    Department of Family Medicine and Primary Healthcare, Hong Kong East Cluster, Hospital Authority, Hong Kong.
  • Daniel Wai-Sing Chu
    Department of Family Medicine and Primary Healthcare, Hong Kong East Cluster, Hospital Authority, Hong Kong.
  • Chung-Wah Siu
    Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong.

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