中文摘要：

针对传统的多尺度时间序列重构方法无法完整地提取脑电图EEG信号特征的问题,提出了一种新的多尺度时间序列重构方法——移动方差化。将EEG信号使用移动方差化方法进行序列重构,进而在多个尺度上提取时间序列的2个特征值——样本熵和方差熵。最后对所提取的特征值使用KS检验方法进行p值检验。实验证明,利用移动方差化方法重构的多个尺度上的时间序列对EEG信号进行特征提取,可以有效地区分癫痫患者发作间期与发作期的EEG信号,为之后利用EEG信号诊断判别精神疾病提供了依据。

英文摘要：

Focused on the inability of traditional multiscale time series reconstruction method to extract EEG signal characteristics completely,a new multiscale reconstruction method of time series,moving variances was proposed.EEG signals are reconstituted by new method of moving variances,then two eigenvalues,sample entropy and variance entropy,are extracted at multiple scales.Finally,the extracted features are tested of p-value by method of KS test.Experiments show extracting EEG signal features from time series at multiple scales which are reconstituted by the method of moving variances can effectively distinguish EEG signals of patients with epilepsy during period and interval of onset and thus provide basis to diagnose mental disorders by EEG signals.