ArticlePatterns (New York, N.Y.)2025
AISleep: Automated and interpretable sleep staging from single-channel EEG data.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hypnogram-Driven Automatic Sleep Staging and a Quality-Index Assessment Through a Two-Stage LSTM-DNN Ensemble Learning Approach Using Multi-Biosignal Features for Sleep Disorder Detection.Sensors (Basel, Switzerland) · 2026Article
- Sleep staging through an unsupervised learning lens.Patterns (New York, N.Y.) · 2025Article
- AISleep: Automated and interpretable sleep staging from single-channel EEG data.Patterns (New York, N.Y.) · 2025Article
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Authors and funding
11 authors.
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Abstract
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.
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