Background Preterm birth is associated with an increased risk for neurodevelopmental impairments, requiring brain monitoring using amplitude-integrated electroencephalography (aEEG). While tools exist to detect severe brain dysfunction, methods for mild dysfunction—such as the Burdjalov scoring system or expert identification of sleep-wake cycles—are limited by subjectivity and require expert training. Existing automated sleep-staging models are typically trained on term neonates using polysomnography, a resource-intensive method not widely feasible in neonatal intensive care units (NICUs) for preterm neonates, where simplified aEEG with fewer electrodes is more commonly used.
Methods aEEG recordings from neurologically healthy neonates between 32 and 42 weeks postmenstrual age (PMA) were annotated for quiet sleep (QS) and non-quiet sleep (NQS) by a single expert clinician.
Results Five classifiers were trained to classify QS and NQS. A k-nearest neighbors model achieved a mean Cohen’s Kappa of 0.71± 0.12 in preterm infants, decreasing to 0.48 ± 0.21 in term infants. Features from QS segments were strongly correlated with PMA, enabling a PMA predictor model to achieve an average error of 0.88 weeks.
Conclusion Although performance on QS/NQS classification was strong for neonates between 33 and 37 weeks PMA, generalization across the full 32–42 week range remains challenging. Nevertheless, the low average error of the PMA predictor highlights its potential as a tool for detecting mild neuromaturation delays.