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Research Letter
November 27, 2019

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

Author Affiliations
  • 1Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 2Division of Cardiology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong SAR, China
  • 3Heart and Vascular Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 4Heart Research Institute, Charles Perkins Centre, and Concord Hospital Cardiology, University of Sydney, Australia
  • 5Cardiio Inc, Cambridge, Massachusetts
JAMA Cardiol. Published online November 27, 2019. doi:https://doi.org/10.1001/jamacardio.2019.4004

Approaches for atrial fibrillation (AF) detection can screen only 1 patient at a time.1 In 2018,2 we demonstrated a novel method of AF detection by analyzing facial photoplethysmographic (FPPG) signals without physical contact using a smartphone camera.2 In this proof-of-concept study, we prospectively evaluated the feasibility of high-throughput AF detection by analyzing FPPG signals3 from multiple patients concurrently using a single digital camera and a pretrained deep convolutional neural network (DCNN).4

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