mlsanityData to 7 Oct 2026

Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy

arXiv 1710.02094 ↗First posted 5 Oct 2017cs.NEcited by 39

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Abstract (arXiv)

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph - a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 instead of 30 second scoring epochs. A T1N marker based on unusual sleep-stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 10; 2023: 6; 2024: 4; 2025: 9; 2026: 5.

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  1. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 83
  2. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 41
  3. 2024SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signalscited by 38
  4. 2021DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimatescited by 26
  5. 2021RobustSleepNet: Transfer learning for automated sleep staging at scalecited by 24
  6. 2022Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformerscited by 13
  7. 2022FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networkscited by 12
  8. 2022U-Sleep's resilience to AASM guidelinescited by 12
  9. 2022Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoringcited by 7
  10. 2022SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learningcited by 7
  11. 2023S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification modelscited by 7
  12. 2025SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging modelscited by 7
27 more
  1. 2022Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguitycited by 6
  2. 2021MSED: a multi-modal sleep event detection model for clinical sleep analysiscited by 5
  3. 2022Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoringcited by 4
  4. 2022A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecturecited by 4
  5. 2023Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classificationcited by 3
  6. 2024Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Healthcited by 3
  7. 2025Leveraging Generic Time Series Foundation Models for EEG Classificationcited by 3
  8. 2026What Do EEG Foundation Models Capture from Human Brain Signals?cited by 3
  9. 2024A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligencecited by 2
  10. 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Modelscited by 2
  11. 2022SERF: Interpretable Sleep Staging using Embeddings, Rules, and Featurescited by 1
  12. 2023aSAGA: Automatic Sleep Analysis with Gray Areascited by 1
  13. 2023Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Reviewcited by 1
  14. 2025PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Stagingcited by 1
  15. 2025AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohortscited by 1
  16. 2022Performance and utility trade-off in interpretable sleep stagingcited by 0
  17. 2023Convolutional Monge Mapping Normalization for learning on sleep datacited by 0
  18. 2023U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Stagingcited by 0
  19. 2024HypomimiaCoach: An AU-based Digital Therapy System for Hypomimia Detection & Rehabilitation with Parkinson's Diseasecited by 0
  20. 2025sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Stagingcited by 0
  21. 2025From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysiscited by 0
  22. 2025NAPS: Attention-Based Fusion of Heterogeneous Physiological Signalscited by 0
  23. 2025NeuroLingua: A Language-Inspired Hierarchical Framework for Multimodal Sleep Stage Classification Using EEG and EOGcited by 0
  24. 2026Physiology as Language: Translating Respiration to Sleep EEGcited by 0
  25. 2026Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasetscited by 0
  26. 2026StageGuard: Physiologically Constrained Sleep Stagingcited by 0
  27. 2026NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classificationcited by 0