Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment.

Journal: Journal of the American Heart Association
Published Date:

Abstract

BACKGROUND: Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring.

Authors

  • Ming-Zher Poh
    Cardiio, Cambridge, Massachusetts, USA.
  • Anthony J Battisti
    iRhythm Technologies San Francisco CA.
  • Li-Fang Cheng
    Verily Life Sciences South San Francisco CA.
  • Janice Lin
    Verily Life Sciences South San Francisco CA.
  • Anil Patwardhan
    Verily Life Sciences South San Francisco CA.
  • Ganesh S Venkataraman
    Colorado Heart and Vascular Lakewood CO.
  • Charles A Athill
    San Diego Cardiac Center San Diego CA.
  • Nimesh S Patel
    University of Texas Southwestern Medical Center Dallas TX.
  • Chinmay P Patel
    University of Pittsburgh Medical Center Harrisburg Harrisburg PA.
  • Christian E Machado
    Ascension Providence Hospital Southfield MI.
  • Jeffrey T Ellis
    iRhythm Technologies San Francisco CA.
  • Lori A Crosson
    iRhythm Technologies San Francisco CA.
  • Yuriko Tamura
    iRhythm Technologies San Francisco CA.
  • R Scooter Plowman
    Verily Life Sciences South San Francisco CA.
  • Mintu P Turakhia
    Department of Medicine and Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.
  • Hamid Ghanbari