Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

Abstract

Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment.