Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 27, 2021

Abstract

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the most frequent asymptomatic arrhythmias associated with significant morbidity and mortality. Identifying the susceptibility to AF based on routine or continuous ECG recording is of considerable interest. Despite several P-wave characteristics and skin sympathetic nerve activity (SKNA) linked to AF onset, neither factor has offered accurate predictability. We propose a deep learning enabled method for AF risk prediction.

Authors

  • Heng-An Tzou
    Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan; Devision for AI Computing Platform, Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan. Electronic address: anantzou.06g@g2.nctu.edu.tw.
  • Shien-Fong Lin
  • Peng-Sheng Chen
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

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