arXiv 1002.45222010-02-24cs.AI

基于EEG的新生儿脑成熟度贝叶斯评估中的特征重要性

Feature Importance in Bayesian Assessment of Newborn Brain Maturity from EEG

University of Bedfordshire（贝德福德大学）

L. Jakaite, V. Schetinin, C. Maple

备注：: Proceedings of the 9th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), University of Cambridge, UK, 2010, edited by L. A. Zadeh et al, pp 191 - 195
发表信息：: Proceedings of the 9th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), University of Cambridge, UK, 2010, edited by L. A. Zadeh et al, pp 191 - 195

摘要：

应用贝叶斯模型平均（BMA）方法从睡眠脑电图中评估新生儿脑成熟度。从理论上讲，这种方法提供了对决策中不确定性的最准确评估。然而，现有的BMA技术已被证明在缺乏一些先验信息的情况下提供有偏差的评估，从而能够在合理的时间内详细探索模型参数空间。细节的缺乏导致从后验分布中不成比例地采样。在脑成熟度的EEG评估的情况下，BMA结果可能是有偏差的，因为缺乏关于EEG特征重要性的信息。在本文中，我们将探讨如何后验信息的EEG功能可以使用，以减少不成比例的采样对BMA性能的负面影响。我们使用睡眠新生儿记录的EEG数据来测试所提出的BMA技术的效率。

英文摘要：

The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a reasonable time. The lack in details leads to disproportional sampling from the posterior distribution. In case of the EEG assessment of brain maturity, BMA results can be biased because of the absence of information about EEG feature importance. In this paper we explore how the posterior information about EEG features can be used in order to reduce a negative impact of disproportional sampling on BMA performance. We use EEG data recorded from sleeping newborns to test the efficiency of the proposed BMA technique.

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