Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

Journal: Sleep medicine
Published Date: Sep 29, 2024

Abstract

OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis.

Authors

  • Dongyeop Kim
    Department of Neurology, Seoul Hospital, College of Medicine, Ewha Womans University, Seoul, 07804, South Korea.
  • Ji Yong Park
    Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea.
  • Young Wook Song
    Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea.
  • Euijin Kim
    Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea.
  • Sungkean Kim
    Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea; Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea. Electronic address: kimsk@hanyang.ac.kr.
  • Eun Yeon Joo
    Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Korea.

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