Pre-Estimation System of Falling Asleep Process Using Distribution-Based Deep Learning Model With Polysomnography

Sleep is associated with mental and physical health; therefore, it is important to pre-assess sleep quality for daily life. However, previous sleep studies, such as self-reporting and sleep stage (SS) classification, have limitations in their ability to assess sleep quality at an early stage. Therefore, a new approach based on the pre-estimation system for sleep quality using a sensor-based model is required. In this study, we propose a distribution-based deep learning model to pre-assess sleep quality using the estimation of the falling asleep process (FAP) at the early SS. Sleep onset latency (SOL) is considered to pre-estimate the FAP because SOL is associated with insomnia and could be observable at the beginning of sleep. Moreover, multimodal polysomnography (PSG) signals, including two-channel electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG), are used to consider the complexity of sleep dynamics. The advantage of the proposed method is combining each distribution from PSG with the importance score to compare influence and show the probability of wake over time. To evaluate the model performance, the feature extraction and temporal encoder with the previous SS module are compared, respectively, and the proposed model achieved the best mean absolute error (MAE) (8.65) and concordance index (C-index) (0.708) among baseline models, including AttnSleep, TinySleepNet, and MEDI-SOL, while maintaining a competitive Brier score (BS) (0.041) and negative binomial log-likelihood (NBLL) (0.145). As a result, the main contribution of this study is that it can pre-assess sleep quality by estimating a probability over time of when a subject will fall asleep.