AISleep: Automated and interpretable sleep staging from single-channel EEG data

Sleep staging is essential for understanding sleep physiology and diagnosing sleep-related disorders. However, traditional manual scoring is time-consuming and resource intensive, limiting its scalability for large-scale application. In this study, we introduce AISleep, an automated and interpretable unsupervised algorithm based on feature-weighted kernel density estimation (KDE), designed to stage sleep using only a single electroencephalogram (EEG) channel. AISleep was evaluated using both public benchmark datasets of healthy subjects and clinical datasets of patients with sleep disorders. It outperforms state-of-the-art (SOTA) unsupervised sleep staging algorithms in young, healthy subjects and demonstrates better generalizability compared to supervised models. Importantly, we observed that some key EEG features decline with age, which may contribute to reduced staging accuracy in older adults. This study presents a robust and interpretable unsupervised sleep staging algorithm with a lightweight design that makes it well suited to integration into portable devices, offering a practical and scalable solution for accurate, home-based sleep monitoring.