ArticlePatterns (New York, N.Y.)2022
Auto-annotating sleep stages based on polysomnographic data.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimising Sleep Stage Detection Using a Minimal Non-EEG Physiological Signal Set and Deep Learning.Journal of sleep research · 2026Article
- Automated analysis of the American Academy of Sleep Medicine Inter-Scorer Reliability gold-standard polysomnogram dataset.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- Article
- GRU-powered sleep stage classification with permutation-based EEG channel selection.Scientific reports · 2024Article
- Sleep stage prediction using multimodal body network and circadian rhythm.PeerJ. Computer science · 2024Article
- Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals.Brain and behavior · 2023Article
- A journey toward artificial intelligence-assisted automated sleep scoring.Patterns (New York, N.Y.) · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
Sleep disorders affect the quality of life, and the clinical diagnosis of sleep disorders is a time-consuming and tedious process requiring recording and annotating polysomnographic records. In this work, we developed an auto-annotation algorithm based on polysomnographic records and a deep learning architecture that predicts sleep stages at the millisecond level. The model improves the efficiency of the polysomnographic record annotation process by automatically annotating each record within 3.8 s of computation time and with high accuracy. Disease-related sleep stages, such as arousal and apnea, can also be identified by this model, which further expands the physiological insights that the model can potentially provide. Finally, we explored the applicability of the model to data collected from a different modality to demonstrate the robustness of the model.
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Registered trials
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