ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.
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 1 paper.
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1 citing paper in PubMed.
- Data pipeline quality: development and validation of a quality assessment tool for data-driven algorithms and artificial intelligence in healthcare.BMJ health & care informatics · 2026Article
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12 authors.
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Abstract
STUDY
objectivesDespite frequent sleep disruption in the pediatric intensive care unit, bedside sleep monitoring in real time is currently not available. Supervised machine learning applied to electrocardiography data may provide a solution, because cardiovascular dynamics are directly modulated by the autonomic nervous system during sleep.
methodsThis retrospective study used hospital-based polysomnography recordings obtained in noncritically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Features were derived in time, frequency, and nonlinear domain from preprocessed electrocardiography data. Sleep classification models were developed for 2, 3, 4, and 5 states using logistic regression, random forest, and XGBoost classifiers during 5-fold nested cross-validation. Models were additionally validated across age categories.
resultsA total of 90 noncritically ill children were included with a median (Q1, Q3) recording length of 549.0 (494.8, 601.3) minutes. The 3 models obtained an area under the receiver operator characteristic curve of 0.72-0.78 with minimal variation across classifiers and age categories. Balanced accuracies were 0.70-0.72, 0.59-0.61, 0.50-0.51, and 0.41-0.42 for 2, 3, 4, and 5 states, respectively. Generally, the XGBoost model obtained the highest balanced accuracy (
conclusionsElectrocardiography-based machine learning models are a promising and noninvasive method for automated sleep classification directly at the bedside of noncritically ill children aged 6 months-18 years. Models obtained moderate-to-good performance for 2- and 3-state classification. CITATION: van Twist E, Meester AM, Cramer ABG, et al. Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.
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