Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 30, 2019

Abstract

BACKGROUND AND OBJECTIVE: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.

Authors

  • Urtnasan Erdenebayar
    Department of Biomedical Engineering, Yonsei University College of Health Science, Wonju, Korea.
  • Yoon Ji Kim
    Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea.
  • Jong-Uk Park
    Department of Biomedical Engineering, Yonsei University, Wonju, Gangwondo, Korea.
  • Eun Yeon Joo
    Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Korea.
  • Kyoung-Joung Lee

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