基于独立分量分析算法研究儿童癫痫脑电的混沌动力学特征

首先采用独立分量分析(Independent component analysis,ICA)算法,将儿童癫痫信号从复杂的背景脑电(Electroencephalogram,EEG)中分离出来;然后采用了一维时间序列相空间重构技术和混沌的定量判据,对分离出来的独立分量信号进行了分析与计算.通过对生理和癫痫状态下独立分量信号的相图、功率谱、关联维数和Lyapunov指数的对比研究,得出如下结论:(1)EEG独立分量的相图、功率谱、关联维数和Lyapunov指数反映了大脑的总体动态特征,它们可作为一种定量指标衡量大脑的健康状态;(2)在正常的生理状态下EEG是混沌的,而在癫痫状态下则趋于有序。

Research on Chaotic Behavior of Epilepsy Electroencephalogram of Children Based on Independent Component Analysis Algorithm

In this paper, Independent component analysis (ICA) was first adopted to isolate the epileptiform signals from the background Electroencephalogram (EEG) signals. Then, by using the phase space reconstruct techniques from a time series and the quantitative criterions and rules of system chaos, different phases of the epileptiform signals were analyzed and calculated. Through the comparative research with the analyses of the phase plots, the power spectra, the computation of the correlation dimensions and the Lyapunov exponents of the physiologyical and the epileptiform signals, the following conclusions were drawn: (1)The phase plots, the power spectra, the correlation dimensions and the Lyapunov exponents of the EEG independent components reflect the general dynamical characteristics of brains, which can be taken as a quantitative index to weigh the healthy states of brains. (2) Under normal physiological conditions, the EEG signals are chaotic, while under epilepsy conditions the signals approach regularity.