Abstract：In order to study the nonlinear dynamics of the schizophrenic patient’s MEG signals in resting-state， this paper presents a method of feature extraction which combined the wavelet variation with the approximate entropy. The brain magnetic signals of 10 controls and 10 patients are decomposed to six levels by wavelet decomposition and wavelet coefficient is extracted corresponding to the θ rhythm and α rhythm of MEG signals. Then the distribution of approximate entropy between two kinds of people are calculated and compared. The experiment results show that the entropy of each brain region and channel of the MEG signals in schizophrenic patients were generally higher than controls under the same situation， especially frontal and central regions in α rhythm. This result provides a guideline for the study of EEG signal characteristics of the patients and establishes the appropriate classification diagnostic model.
黄晓霞,王盼盼. 基于近似熵快速算法的静息态脑磁信号分析[J]. 华中师范大学学报(自然科学版), 2017, 51(3): 309-316.
HUANG Xiaoxia,WANG Panpan. Resting-state magnetic signals analysis based on the fast algorithm of approximate entropy. journal1, 2017, 51(3): 309-316.
2017, Vol. 51 
