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本文利用多尺度排列熵对正常脑电信号和癫痫脑电信号进行了详细的分析和比较,研究了脑电图信号多尺度排列熵值和年龄的关系以及尺度因子对多尺度排列熵值的影响.通过对处于各个年龄段的22组正常人和22组患有癫痫人群的脑电图进行多尺度排列熵分析,发现在相同年龄段的人群中,正常脑电信号的多尺度排列熵值要高于癫痫脑电信号,熵值平均高出约0.19,约7.9%.另外,在尺度因子小于15的情况下,对于在30到35的年龄段正常人群,其多尺度排列熵值最大,随着年龄段的增大或降低熵值都一定程度的降低.结果证明,多尺度排列熵可以成功区分正常脑电信号和癫痫脑电信号,并且熵值可以正确地反映人体大脑发育的一般过程.
In this paper, the multi-scale permutation entropy is used to analyze and compare the EEG and epileptic EEG in detail. The relationship between multi-scale entropy of EEG signals and age and the influence of scale factor on the multi-scale permutation entropy are studied By multiscale entropy analysis of EEG in 22 normal subjects and 22 subjects with epilepsy in all age groups, it was found that the multiscale entropy of normal EEG in the same age group Higher than that of epileptic EEG, the average entropy value is about 0.19, about 7.9% .In addition, when the scale factor is less than 15, the multiscale entropy value is the highest for the normal population aged 30 to 35 The results show that multi-scale permutation entropy can distinguish normal EEG signals from epileptic EEG signals, and the entropy value can correctly reflect the general process of human brain development.