Wang Xingyuan, Meng Juan, Qiu Tianshuang
School of Electronic & Information Engineering, Dalian University of Technology, Dalian 116024, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2007 Aug;24(4):835-41.
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.
本文首先采用独立成分分析（ICA）从背景脑电图（EEG）信号中分离出癫痫样信号。然后，利用时间序列的相空间重构技术以及系统混沌的定量判据和规则，对癫痫样信号的不同阶段进行分析和计算。通过与生理信号和癫痫样信号的相图分析、功率谱分析、关联维数计算以及李雅普诺夫指数计算的对比研究，得出以下结论：（1）EEG独立成分的相图、功率谱、关联维数和李雅普诺夫指数反映了大脑的一般动力学特征，可作为衡量大脑健康状态的定量指标。（2）在正常生理条件下，EEG信号是混沌的，而在癫痫状态下信号趋于规则。
