Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis

Sleep plays an extremely important role in human health. Ensuring an adequate amount of high-quality sleep is essential for maintaining physical health and psychological balance. Professional measurement of sleep state is mainly conducted through Polysomnography (PSG) [1]. However, PSG involves a significant physical burden on the subjects and is difficult to measure without specialized facilities or hospitals. In recent years, evaluation methods utilizing wearable devices have been developed with the aim of facilitating sleep assessment [2]. However, the information that can be obtained through a smartwatch is limited, typically encompassing data such as acceleration and heart rate. While EEG-based sleep monitoring offers high accuracy, the requirement to wear headgear, even for a single-channel EEG headset [3], presents a significant burden.