Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.

Study Objectives: Despite 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.

Methods: This 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.

Results: A 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 ( < .05), except for 5 states for which logistic regression excelled ( = .67).

Conclusions: Electrocardiography-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. 2025;21(2):261-268.