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Sleep state classification is vital in managing and understanding sleeppatterns and is generally the first step in identifying acute or chronic sleepdisorders. However, it is essential to do this without affecting the naturalenvironment or conditions of the subject during their sleep. Techniques such asPolysomnography(PSG) are obtrusive and are not convenient for regular sleepmonitoring. Fortunately, The rise of novel technologies and advanced computinghas given a recent resurgence to monitoring sleep techniques. One suchcontactless and unobtrusive monitoring technique is Ballistocradiography(BCG),in which vitals are monitored by measuring the body's reaction to the cardiacejection of blood. In this study, we propose a Multi-Head 1D-Convolution basedDeep Neural Network to classify sleep-wake state and predict sleep-wake timeaccurately using the signals coming from a BCG sensor. Our method achieves asleep-wake classification score of 95.5%, which is on par with researches basedon the PSG system. We further conducted two independent studies in a controlledand uncontrolled environment to test the sleep-wake prediction accuracy. Weachieve a score of 94.16% in a controlled environment on 115 subjects and94.90% in an uncontrolled environment on 350 subjects. The high accuracy andcontactless nature of the proposed system make it a convenient method for longterm monitoring of sleep states.

Classification Of Sleep-Wake State In A Ballistocardiogram System Based On Deep Learning