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

Journal: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Published Date: Feb 1, 2025

Abstract

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.

Authors

  • Eris van Twist
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Anne M Meester
    Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands.
  • Arnout B G Cramer
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Matthijs de Hoog
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Alfred C Schouten
    BioMechanical Engineering Department, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.
  • Sascha C A T Verbruggen
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Koen F M Joosten
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Maartje Louter
    Division of Clinical Neurophysiology, Department of Neurology, Erasmus MC, Rotterdam, The Netherlands.
  • Dirk C G Straver
    Division of Clinical Neurophysiology, Department of Neurology, Erasmus MC, Rotterdam, The Netherlands.
  • David M J Tax
    Pattern Recognition Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands.
  • Rogier C J de Jonge
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Jan Willem Kuiper
    Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.

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