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Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms

Research Output:
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Abstract

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.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Article number

629654

Journal (Volume, Issue Number)

Computational and Mathematical Methods in Medicine

Publication milestones

  • Published - 07/03/2012

Publication status

Published - 07/03/2012

ISSN

1748-670X

Publication IDs

  • handle.net: 10547/293079
  • Scopus: 84859743401

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