Sleep Quality Classification Based on VAE-LSTM Using Sleep Sounds and Sleep Personality Analysis with TimeSHAP

Abstract

In recent years, as interest in health increases, methods have been devised to enable individuals to monitor their sleep status at home. Compared to conventional methods using smartwatches, sensors, etc., methods using sleep sounds have the advantages of being inexpensive, non-contact, and capable of detecting many biological activities. In this study, we aim to construct a machine-learning sleep evaluation model using sleep sounds that can provide evidence.In this study, sleep sound events were first extracted from overnight sleep sounds. Next, latent expressions of sleep sound events were extracted using VAE, and clustered using GMM. Then, we trained an LSTM that estimates the subjective evaluation of sleep using the obtained probability of belonging to each cluster as input data and obtained a sleep evaluation model. Finally, we applied TimeSHAP, a method for interpreting time series prediction models, to the sleep evaluation model to examine the importance of each cluster in sleep evaluation.The experimental results showed that for a given subject, a 94.8% accuracy rate was achieved in determining whether the subject was sleeping well or not for a single night. TimeSHAP, a temporal extension of SHAP, revealed that the types and times of sound events that influence the determination of sleep quality varied from person to person.