A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age

TL;DR

This paper introduces a Bayesian Network model that directly estimates brain-age from EEG sleep data in preterm infants, effectively capturing sleep and age dependencies to improve prediction accuracy across a broad age range.

Contribution

A novel Bayesian approach that unifies sleep staging and brain-age prediction, overcoming limitations of traditional methods reliant on subjective sleep labels.

Findings

Improved accuracy of brain-age prediction over a wide age range.

Effective modeling of sleep and age dependencies in EEG data.

Potential for earlier detection of developmental deviations.

Abstract

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.