ArticleSleep2017
Large-Scale Automated Sleep Staging.
Article in Sleep, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Analytical amplitude/phase markers for neurological sleep apnea events.Neurobiology of sleep and circadian rhythms · 2026Article
- 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
- Brain Health from Sleep EEG: A Multicohort, Deep Learning Biomarker for Cognition, Disease, and Mortality.NEJM AI · 2026Article
- Ordinal Sleep Depth: A Data-Driven Continuous Measurement of Sleep Depth.Journal of sleep research · 2026Article
- Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Article
- [Research progress in electroencephalogram-based brain age prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Article
- Generalized sleep decoding with basal ganglia signals in multiple movement disorders.NPJ digital medicine · 2024Article
- Article
- Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.PLoS computational biology · 2024Article
- Article
- Decoding information about cognitive health from the brainwaves of sleep.Scientific reports · 2023Article
- The sleep and wake electroencephalogram over the lifespan.Neurobiology of aging · 2023Article
- Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.Journal of medical Internet research · 2023Article
- Article
- An accessible and versatile deep learning-based sleep stage classifier.Frontiers in neuroinformatics · 2023Article
- Functional extreme learning machine.Frontiers in computational neuroscience · 2023Article
- Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.Frontiers in artificial intelligence · 2023Article
- Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.Medicina (Kaunas, Lithuania) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Study Objectives: Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learning methods can approximate the performance of human scorers when supplied with sufficient training cases and to investigate how staging performance depends on the number of training patients, contextual information, model complexity, and imbalance between sleep stage proportions. Methods: A total of 102 features were extracted from six electroencephalography (EEG) channels in routine polysomnography. Two thousand nights were partitioned into equal (n = 1000) training and testing sets for validation. We used epoch-by-epoch Cohen's kappa statistics to measure the agreement between classifier output and human scorer according to American Academy of Sleep Medicine scoring criteria. Results: Epoch-by-epoch Cohen's kappa improved with increasing training EEG recordings until saturation occurred (n = ~300). The kappa value was further improved by accounting for contextual (temporal) information, increasing model complexity, and adjusting the model training procedure to account for the imbalance of stage proportions. The final kappa on the testing set was 0.68. Testing on more EEG recordings leads to kappa estimates with lower variance. Conclusion: Training with a large data set enables automated sleep staging that compares favorably with human scorers. Because testing was performed on a large and heterogeneous data set, the performance estimate has low variance and is likely to generalize broadly.
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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.