Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

Journal: Journal of Korean medical science
Published Date: Feb 5, 2024

Abstract

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR.

Authors

  • Jiwoong Kim
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
  • Sun Jung Lee
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
  • Bonggyun Ko
    Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.
  • Myungeun Lee
    Department of Cardiovascular Medicine, Chonnam National University Hospital, Gwangju, Korea.
  • Young-Shin Lee
    Seers Technology Co., Ltd., Pyeongtaek, Korea. luke.lee@seerstech.com.
  • Ki Hong Lee
    The Heart Center of Chonnam National University Hospital, 42 Jaebongro, Dong-gu, Gwangju 501-757, South Korea.

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