EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

Journal: Biomedical engineering online
PMID: 38902671

Abstract

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets.

Authors

  • Meng-Hsuan Liu
    Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan.
  • Shang-Yu Chien
    Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan.
  • Ya-Lun Wu
    Artificial Intelligence Center, China Medical University Hospital, No. 2, Yude Rd, North Dist, Taichung, Taiwan.
  • Ting-Hsuan Sun
    Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan.
  • Chun-Sen Huang
    Sleep Medicine Center, Department of Pulmonary and Critical Care Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung, Taiwan.
  • Kai-Cheng Hsu
    Bioinformatics Section, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD, United States; Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
  • Liang-Wen Hang
    Sleep Medicine Center, Department of Pulmonary and Critical Care Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung, Taiwan. lungwen.hang@gmail.com.

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