ArticleFrontiers in neurology2023
Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- Temporally Continuous Automated Sleep-Wake Classification Using Deep Learning.medRxiv : the preprint server for health sciences · 2025Article
- Validation of manually scored multichannel frontal electroencephalography against polysomnography in a paediatric cohort.Journal of sleep research · 2025Article
- Article
- Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.Frontiers in medicine · 2025Article
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
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Abstract
Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset ( Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic ( Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.
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