Deep learning-based automated diagnosis of obstructive sleep apnea and sleep stage classification in children using millimeter-wave radar and pulse oximeter.

Journal: Sleep health
Published Date: Jul 29, 2025

Abstract

STUDY OBJECTIVES: Due to the high cost, complexity, and workload of polysomnography, a radar-based sleep monitoring device, QSA600, has been developed as a more simplified alternative for children. This study evaluates its agreement with polysomnography for obstructive sleep apnea diagnosis and sleep staging.

Authors

  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Ruobing Song
    Department of Respiratory, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Yunxiao Wu
  • Li Zheng
    School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Wenyu Zhang
    School of Nursing, Dalian University, Dalian, Liaoning, China.
  • Zhaoxi Chen
    Beijing Tsingray Technology Co. Ltd., Beijing, China.
  • Gang Li
    The Centre for Cyber Resilience and Trust, Deakin University, Australia.
  • Zhifei Xu

Keywords

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