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

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.

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

SUBMITTER: van Twist E 

PROVIDER: S-EPMC11789255 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature