Importance of EEG features in active and quiet sleep for assessment of newborn brain maturation at neonatal centres

Newborn brain development can be analysed and interpreted by EEG-experts scoring maturity-related features in sleep electroencephalogram (EEG). These features widely vary during the sleep hours, and their importance can be different in quiet and active sleep stages. The level of muscle and electrode artefacts during the active sleep stage is higher than that in the quiet stage that could reduce the importance of features extracted from the active stage. In this paper, we use Bayesian methodology of averaging over Decision Tree (DT) models to assess the newborn brain maturity and explore importance of EEG features extracted from the quiet and active sleep stages. The use of DT models enables to find the EEG features which are most important for the brain maturity assessment. The method has been verified on EEG data recorded from 995 patients of neonatal centres under a project of the University of Jena (Germany) in 2004. The research has been supported by the Leverhulme Trust (UK), and anonymised EEG recordings have been made available for public research under support of the University of Bedfordshire (UK).