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Updated: Jun 12, 2025
Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children
Eris van Twist1, Anne M Meester2, Arnout B G Cramer1
1Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
Insights
Machine learning models using electrocardiography (ECG) data can now classify sleep in children noninvasively. This offers a promising bedside tool for monitoring sleep patterns in noncritically ill pediatric patients.
Area of Science:
- Biomedical Engineering
- Computational Neuroscience
- Pediatric Sleep Medicine
Background:
- Real-time bedside sleep monitoring is unavailable in pediatric intensive care units, despite frequent sleep disruption.
- Cardiovascular dynamics are influenced by the autonomic nervous system during sleep, suggesting ECG as a potential data source.
Purpose of the Study:
- To develop and validate supervised machine learning models for automated sleep classification using electrocardiography (ECG) data in children.
- To assess the performance of these models across different age groups and sleep state classifications.
Main Methods:
- Retrospective analysis of polysomnography recordings from 90 noncritically ill children (6 months to 18 years).
- Extraction of ECG features in time, frequency, and nonlinear domains.
- Development of 2- to 5-state sleep classification models using logistic regression, random forest, and XGBoost, with 5-fold nested cross-validation.
Main Results:
- Models achieved an area under the receiver operator characteristic curve of 0.72-0.78.
- Balanced accuracies ranged from 0.70-0.72 for 2-state to 0.41-0.42 for 5-state classification.
- XGBoost generally performed best, except for 5-state classification where logistic regression excelled.
Conclusions:
- ECG-based machine learning models provide a promising, noninvasive method for bedside sleep classification in noncritically ill children.
- The models demonstrated moderate-to-good performance for 2- and 3-state sleep classification in this pediatric population.
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 (P < .05), except for 5 states for which logistic regression excelled (P = .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. J Clin Sleep Med. 2025;21(2):261-268.
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