Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome.

Journal: JACC. Clinical electrophysiology
PMID: 35981788

Abstract

BACKGROUND: The diagnosis of Brugada syndrome by 12-lead electrocardiography (ECG) is challenging because the diagnostic type 1 pattern is often transient.

Authors

  • Shun Liao
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Mahmoud Bokhari
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Praloy Chakraborty
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Adrian Suszko
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Gavin Jones
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Danna Spears
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Michael Gollob
    Peter Munk Cardiac Center, Division of Cardiology, Toronto General Hospital, University Health Network, Toronto, Canada.
  • Zhaolei Zhang
    Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Vijay S Chauhan
    Division of Cardiology, Peter Munk Cardiac Center, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada. Electronic address: vijay.chauhan@uhn.ca.

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