An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children

Study Objectives: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification. Methods: Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital -based polysomnography 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. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five -fold nested cross -validation. Model performance was assessed across age categories and electroencephalography channels. Results: In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4 -A1 and F3 -A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four -state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three -state classification, respectively. Conclusions: We propose an interpretable and generalizable sleep index derived from single -channel electroencephalography for automated sleep monitoring at the bedside in non -critically ill children ages 6 months to 18 years, with good performance for two- and three -state classification.