Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK.
Comput Math Methods Med. 2012;2012:629654. doi: 10.1155/2012/629654. Epub 2012 Mar 7.
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.
新生儿大脑成熟度可以通过专家分析多导睡眠图中可识别的与成熟度相关的模式来评估。自 36 周以来，这些模式中的大多数在 EEG 中变得可识别，尤其是在通过两个中央颞通道记录的 EEG 中。使用这种 EEG 记录可以使专家最小化睡眠干扰、准备时间和运动伪影。我们假设，年龄在 36 周及以上的新生儿的大脑成熟度可以像通过对完整多导睡眠图信息的专家分析一样，从 2 通道睡眠 EEG 中自动评估。我们使用贝叶斯推理来检验这一假设，并帮助专家获得 EEG 评估的完整概率信息。贝叶斯方法可以通过在高后验概率密度区域进行蒙特卡罗积分来实现，但现有的技术往往会在缺乏详细探索模型空间所需的先验信息的情况下提供有偏差的评估，而在合理的时间内做到这一点是不现实的。在本文中，我们旨在利用 EEG 特征的后验信息来减少评估中的可能偏差。拟议方法的性能在一组 EEG 记录上进行了测试。
