ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Deciphering Insomnia: Benchmarking Automated Sleep Staging Algorithms for Complex Sleep Disorders.Journal of sleep research · 2025Article
- Decoding brain age predictions from sleep electroencephalography across infancy to adolescence.Scientific reports · 2025Article
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8 authors.
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Abstract
STUDY
objectivesU-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to (1) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and (2) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3,114 polysomnograms from a tertiary center.
methodsAgreement between U-Sleep and "gold" 30-second epoch sleep staging was determined across both datasets. Utilizing the concordance dataset, the hypothesis of equivalence between human scorers and U-Sleep was tested using a Wilcoxon 2 1-sided test. Multivariable regression and generalized additive modeling were used on the clinical dataset to estimate the effects of age, comorbidities, and polysomnographic findings on U-Sleep performance.
resultsThe median (interquartile range) Cohen's kappa agreement of U-Sleep and individual trained humans relative to "gold" scoring for 5-stage sleep staging in the concordance dataset were similar, kappa = 0.79 (0.19) vs 0.78 (0.13), respectively, and satisfied statistical equivalence (2 1-sided test
conclusionsWhile U-Sleep algorithms showed statistically equivalent performance to trained scorers, accuracy was lower in children < 2 years and those with sleep-disordered breathing or comorbidities affecting electroencephalography. U-Sleep is suitable for pediatric clinical utilization provided automated staging is followed by expert clinician review. CITATION: Kevat A, Steinkey R, Suresh S, et al. Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network.
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