Supervised machine learning on ECG features to classify sleep in non-critically ill children.

Autor:	van Twist E; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., Meester AM; Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands., Cramer ABG; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., de Hoog M; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., Schouten AC; Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands., Verbruggen SCAT; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., Joosten KFM; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., Louter M; Department of Neurology and Clinical Neurophysiology, Erasmus MC, Rotterdam, The Netherlands., Straver DCG; Department of Neurology and Clinical Neurophysiology, Erasmus MC, Rotterdam, The Netherlands., Tax DMJ; Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands., de Jonge RCJ; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands., Kuiper JW; Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine [J Clin Sleep Med] 2024 Sep 27. Date of Electronic Publication: 2024 Sep 27.
DOI:	10.5664/jcsm.11358
Abstrakt:	Study Objectives: Despite frequent sleep disruption in the paediatric intensive care unit (PICU), bedside sleep monitoring in real-time is currently not available. Supervised machine learning (ML) applied to electrocardiography (ECG) data may provide a solution, since cardiovascular dynamics are directly modulated by the autonomic nervous system (ANS) during sleep. Methods: Retrospective study using hospital-based polysomnography (PSG) recordings obtained in non-critically 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 non-linear domain from pre-processed ECG data. Sleep classification models were developed for two, three, four and five state using logistic regression (LR), random forest (RF) and XGBoost (XGB) classifiers during five-fold nested cross-validation. Models were additionally validated across age categories. Results: A total of 90 non-critically ill children were included with a median (Q1, Q3) recording length of 549.0 (494.8, 601.3) minutes. The three models obtained AUROC 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 two, three, four and five state. Generally, the XGB model obtained the highest balanced accuracy (p < 0.05), except for five state where LR excelled (p = 0.67). Conclusions: ECG-based ML models are a promising and non-invasive method for automated sleep classification directly at the bedside of non-critically ill children aged 6 months to 18 years. Models obtained moderate-to-good performance for two and three state classification. (© 2024 American Academy of Sleep Medicine.)
Databáze:	MEDLINE
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