Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.

Journal: Sleep medicine
PMID: 38232604

Abstract

BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening.

Authors

  • Soonhyun Yook
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA.
  • Dongyeop Kim
    Department of Neurology, Seoul Hospital, College of Medicine, Ewha Womans University, Seoul, 07804, South Korea.
  • Chaitanya Gupte
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA.
  • Eun Yeon Joo
    Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Korea.
  • Hosung Kim
    Laboratory of Neuro Imaging, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA. Electronic address: hosung.kim@loni.usc.edu.

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