  Preterm newborns undergo various stresses that may materialize as learning
problems at school-age. Sleep staging of the Electroencephalogram (EEG),
followed by prediction of their brain-age from these sleep states can quantify
deviations from normal brain development early (when compared to the known
age). Current automation of this approach relies on explicit sleep state
classification, optimizing algorithms using clinician visually labelled sleep
stages, which remains a subjective gold-standard. Such models fail to perform
consistently over a wide age range and impacts the subsequent brain-age
estimates that could prevent identification of subtler developmental
deviations. We introduce a Bayesian Network utilizing multiple Gaussian Mixture
Models, as a novel, unified approach for directly estimating brain-age,
simultaneously modelling for both age and sleep dependencies on the EEG, to
improve the accuracy of prediction over a wider age range.
