[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.
Wang
Xingyuan
X
School of Electronic & Information Engineering, Dalian University of Technology, Dalian 116024, China. wangxy@dlut.edu.cn
Meng
Juan
J
Qiu
Tianshuang
T
D000465:Algorithms; D002648:Child; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted