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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 629654, 7 pages
Research Article

Bayesian Assessment of Newborn Brain Maturity from Two-Channel Sleep Electroencephalograms

Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK

Received 30 September 2011; Accepted 22 December 2011

Academic Editor: Henggui Zhang

Copyright © 2012 Livija Jakaite et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Newborn brain maturity can be assessed by expert analysis of maturity-related patterns recognizable in polysomnograms. Since 36 weeks most of these patterns become recognizable in EEG exclusively, particularly, in EEG recorded via the two central-temporal channels. The use of such EEG recordings enables experts to minimize the disturbance of sleep, preparation time as well as the movement artifacts. We assume that the brain maturity of newborns aged 36 weeks and older can be automatically assessed from the 2-channel sleep EEG as accurately as by expert analysis of the full polysomnographic information. We use Bayesian inference to test this assumption and assist experts to obtain the full probabilistic information on the EEG assessments. The Bayesian methodology is feasibly implemented with Monte Carlo integration over areas of high posterior probability density, however the existing techniques tend to provide biased assessments in the absence of prior information required to explore a model space in detail within a reasonable time. In this paper we aim to use the posterior information about EEG features to reduce possible bias in the assessments. The performance of the proposed method is tested on a set of EEG recordings.