基于复杂性测度的EEG初步研究

利用两种复杂性测度的方法对正常人和病人不同大脑负荷状态下的 EEG进行了分析。一种是 Kaspar和 Schuster定义的复杂度算法 ,一种是新的度量序列复杂度的统计方法 -近似熵。通过对若干例在四种不同实验状态下的 EEG信号的分析 ,表明可通过两种算法的数值变化有效地分辨大脑的状态 :正常或病理以及不同的负荷状态。而且两种复杂性测度算法的变化规律相同。显示出两种复杂性测度的算法在 EEG序列的研究和临床诊断中有广阔的应用前景

Analysis of EEG Based on the Complexity Measure

EEG represents the electric activity of neurons in human brain; it is of course repeatedly used for studying and analyzing the brain activity and the status of brain function. In this paper ,we analyzed the patients' and normal persons' EEG in different physiological state, with the aid of two algorithms as a complexity measure. One is Kc complexity defined by Kaspar and Schuster, the other is a new statistical method to measure complexity sequences Approximate entropy (ApEn).In our work, we analyzed two groups of persons' EEG. Six subjects in 4 different experimental condition are reported. From the results we can discriminate the different state of brain effectively :normal, being injured, and various thinking state.The result suggests that the two algorithms as a complexity measure could be regarded as valued methods in the study of EEG time series and clinical diagnosis.