Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis
Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis
复制标题
多尺度排列熵分析失神脑电图记录的动态特征
DOI：
10.1016/j.eplepsyres.2012.11.003
复制
发表时间：
2013-05-01
影响因子：
2.2
通讯作者：
Li, Xiaoli
中科院分区：
文献类型：
--
作者：
Ouyang, Gaoxiang;Li, Jing;Li, Xiaoli
理解大脑活动向癫痫前发作的转变是一个挑战。本研究采用多尺度排列熵（MPE）来描述不同癫痫发作状态下脑电图记录的动态特征。通过一系列实验对线性判别分析的MPE测度的分类能力进行了评价。与准确率为86.1%的传统多尺度熵方法相比，MPE的分类率为90.6%。实验结果表明，从无癫痫发作状态到癫痫发作状态，脑电图的排列熵有所降低。此外，MPE脑电数据的动态特性可以识别癫痫发作前、无癫痫发作状态和癫痫发作状态的差异。这也支持了脑电图在失神发作前有可检测到的变化的观点。(C) 2012 Elsevier B.V.版权所有
Understanding the transition of brain activities towards an absence seizure, called pre-epileptic seizure, is a challenge. In this study, multiscale permutation entropy (MPE) is proposed to describe dynamical characteristics of electroencephalograph (EEG) recordings on different absence seizure states. The classification ability of the MPE measures using linear discriminant analysis is evaluated by a series of experiments. Compared to a traditional multiscale entropy method with 86.1% as its classification accuracy, the classification rate of MPE is 90.6%. Experimental results demonstrate there is a reduction of permutation entropy of EEG from the seizure-free state to the seizure state. Moreover, it is indicated that the dynamical characteristics of EEG data with MPE can identify the differences among seizure-free, pre-seizure and seizure states. This also supports the view that EEG has a detectable change prior to an absence seizure. (C) 2012 Elsevier B.V. All rights reserved.