A deep learning-based algorithm for detection of cortical arousal during sleep.

Journal: Sleep
Published Date: Dec 14, 2020

Abstract

STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep.

Authors

  • Ao Li
    Beijing University of Chinese Medicine, Beijing, China.
  • Siteng Chen
    Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Stuart F Quan
    Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
  • Linda S Powers
    Department of Electrical and Computer Engineering, College of Engineering, University of Arizona, Tucson, AZ.
  • Janet M Roveda
    Department of Electrical and Computer Engineering, College of Engineering, University of Arizona, Tucson, AZ.

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