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Yanjun Li, Xiaoying Tang, Zhi Xu. Deep Sleep Detection Using Only Respiration[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(3): 459-467. doi: 10.15918/j.jbit1004-0579.17055
Citation: Yanjun Li, Xiaoying Tang, Zhi Xu. Deep Sleep Detection Using Only Respiration[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(3): 459-467.doi:10.15918/j.jbit1004-0579.17055
shu

Deep Sleep Detection Using Only Respiration

doi:10.15918/j.jbit1004-0579.17055
  • 1.

    Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, the Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China;State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China

  • 2.

    Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, the Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2017-04-09
    Abstract
  • Although polysomnogram (PSG) is the gold standard method for the evaluation of sleep quality, it becomes very difficult to clean the residual conductive gel in the hair after collecting brain electricity in the space weightlessness environment. This paper explores the feasibility of detecting deep sleep by using respiratory signal alone. Respiratory signals of oronasal airflow and abdomen movements were analyzed on ten healthy subjects from an open-access sleep dataset, namely ISRUC-Sleep. Deep sleep segments were detected by linear support-vector machine (LSVM) with three indices, including the amplitude variability in the time domain, the energy ratio of main respiratory band in the frequency domain, and the information entropy in the time-frequency domain. The Cohen's Kappa coefficients were 0.43, 0.41 and 0.45 by general LSVM with feature vectors derived from oronasal airflow, abdomen movements and both respiration above, respectively. Moreover, the corresponding Cohen's Kappa coefficients were 0.48, 0.41 and 0.49 by individual LSVM, respectively. Respiration-based method can achieve a moderate accuracy for the detection of deep sleep, with individual LSVM a little better than the general LSVM. Using this approach, detecting deep sleep automatically is attainable by respiratory signals from unconstrained and contact-free measurement. It can be applied to the sleep monitoring for astronauts on orbit.
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