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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
Two measures of complexity were used to analyze EEG under different brain load in normal subjects and patients. One is the complexity algorithm defined by Kaspar and Schuster and the other is the new statistical method of complexity of measurement sequence - approximate entropy. The analysis of several cases of EEG signals in four different experimental states shows that the state of the brain can be distinguished effectively by numerical changes of two algorithms: normal or pathological and different state of load. And the two kinds of complexity measure algorithms have the same change rule. Algorithms that show two measures of complexity have broad applications in the research and clinical diagnosis of EEG sequences