A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of poincaré plots and deep learning.

Journal: Journal of electrocardiology
Published Date: Nov 4, 2022

Abstract

BACKGROUND: Mobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives.

Authors

  • Peter Doggart
    PulseAI, Belfast, United Kingdom.
  • Alan Kennedy
    PulseAI, Belfast, United Kingdom.
  • Raymond Bond
    Ulster University, School of Computing, York St, Northern Ireland.
  • Dewar Finlay
    Nanotechnology and Integrated Bioengineering Centre, Ulster University, Jordanstown, Northern Ireland, United Kingdom.
  • Stephen W Smith
    Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA.

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