正常和癫痫脑电信号之间非线性程度差异

利用量化延迟向量方差方法研究了正常和癫痫脑电信号之间非线性程度的差异,并比较了不同替代数据产生方法得到的脑电信号的非线性度结果。比较结果表明正常和癫痫脑电信号虽然均具有非线性,但癫痫脑电信号的非线性程度要强于正常脑电信号。据此,提出正常和癫痫脑电信号之间的非线性程度差异可以用作描述癫痫发作的特征。

Difference of degree of nonlinearity between normal and epileptic EEG signals

The difference of degree of nonlinearity between normal and epileptic electroencephalogram (EEG) signals was investigated by quantified delay vector variance (DVV) method. The quantified DVV method can be used to determine the degree of nonlinearity of the analyzed time series based on surrogate data. We compare the results of the degree of nonlinearity of EEG signals obtained by different surrogate data generation methods, namely Iterative Amplitude Adjusted Fourier Transformation (IAAFT) and Phase Randomization (PR). It is shown that there exists difference between the degrees of nonlinearity of the EEG signals obtained by the two surrogate data generation methods. However, results obtained by the two surrogate data generation methods show that both normal and epileptic signals are nonlinear, and the degree of nonlinearity of epileptic EEG signals is higher than that of normal EEG signals. Therefore, it is proposed that the difference of degree nonlinearity between normal and epileptic EEG signals can be used as the feature for the detection of epileptic seizure.