Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic characteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
