Assessment and Confidence Estimates of Newborn Brain Maturity from Sleep EEG

Abstract

Electroencephalograms (EEGs) recorded from sleeping newborns contain information about their brain maturity. Although these EEGs are very weak and distorted by artifacts, and widely vary during sleep hours as well as between patients, the main maturity-related patterns are recognizable by experts. However, experts are typically incapable of quantitatively providing accurate estimates of confidence in assessments. The most accurate estimates are, in theory, provided with the Bayesian methodology of probabilistic inference which has been practically implemented with Markov Chain Monte Carlo (MCMC) integration over a model parameter space. Typically this technique aims to approximate the integral by sampling areas of interests with high likelihood of the true model. In practice, the likelihood distributions are typically multimodal, and for this reason, the existing MCMC techniques have been shown incapable of providing the proportional sampling of multiple areas of interest. Besides, the lack of prior information increases this problem especially for a large model parameter space, making its detailed exploration impossible within a reasonable time. Specifically, the absence of information about EEG features has been shown affecting the results of the Bayesian assessment of EEG maturity. In this chapter, authors discuss how the posterior information can be used in order to mitigate the problem of disproportional sampling in order to improve the accuracy of assessments. Having analyzed the posterior information, they found that the MCMC integration tends to oversample the areas in which a model parameter space includes EEG features making a weak contribution to the assessment. This observation has motivated the authors to cure the results of MCMC integration, and when they tested the proposed method on the EEG recordings, they found an increase in the accuracy of assessment.