ArticleNature communications2018
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04227873 (Sleep and Cognition After Ambulatory Hip and Knee Arthroplasty), which is not on this map. Cited by 139 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Sleep and Cognition After Ambulatory Hip and Knee Arthroplasty
Who cites it
139 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.European journal of neurology · 2026Pooled it
- Multichannel machine learning for polysomnographic diagnosis of obstructive sleep apnea: a Bayesian meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Pooled it
- The association between slow wave activity and memory improvement following cognitive-behavioral therapy for insomnia in older adults: A secondary analysis of a randomized clinical trial.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Trial
- Trial
- Article
- Automated Multimodal Sleep Staging Using DWT-Based Wavelet Decomposition and Explainable Machine Learning with Signal Sculpting Topographies.Diagnostics (Basel, Switzerland) · 2026Article
- SleepStageNet: A Lightweight and Explainable Deep Learning Architecture for Multi-Channel Sleep Staging.Brain sciences · 2026Article
- Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices.Journal of medical Internet research · 2026Article
- Observational
- Sleep EEG foundation models reveal within-stage microstructure that improves health screening beyond traditional stages.Research square · 2026Article
- From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis.Scientific reports · 2026Article
- Article
- Article
- YASA automated sleep staging performance across seven nights of normal sleep and sleep restriction.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Article
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Article
- Review
- Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.Journal of family medicine and primary care · 2026Article
- Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Nature communications · 2026Article
79 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors.
Funding
Abstract
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 s instead of 30 s 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.