人工知能学会

Objective sleep deprivation detection can enhance workplace safety and productivity in professions that require long working hours. To address this, we proposed deep learning models for classifying sleep-deprived individuals using EEG data. In this study, we utilized resting-state EEG data collected from both sleep-deprived and well-rested participants and generated five datasets (EyesClosed, EyesOpen-Raw, EyesOpen-AR, EyesClosed+EyesOpen-Raw, and EyesClosed+EyesOpen-AR), then applied them to 1D CNN and 1D CNN-LSTM models. Both models achieved their peak performance with EyesOpen-AR, which slightly outperformed EyesOpen-Raw, while demonstrating comparable performance across all datasets. Applying feature extraction using differential entropy within delta, theta, alpha, and beta bands to the five datasets resulted in decreased performance. The results suggest that artifact-removal from EyesOpen-Raw is not essential for sleep deprivation detection using deep learning models. Additionally, they suggest that 1D CNN may be a more suitable choice for sleep deprivation detection, and non-feature-extracted data is more suitable than feature-extracted data.