Deep Sleep Detection Using Only Respiration
Deep Sleep Detection Using Only Respiration

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.
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