摘要
对麻醉的SD大鼠在癫痫发作前后两种状态的皮层脑电 (ECoG)的时间序列 ,用多种有效的方法和分析技术 ,使得大量的ECoG时间序列得以正确的分析 ,并得出重要的结论 .首先利用延时坐标法重构吸引子 ;计算互信息函数 ,取互信息函数第一次达到最小值的延时为重构延时时间 ,提出将伪邻点法和Cao法相结合的方法确定最佳嵌入维数 .然后采用非线性预报和替代数据法相结合的方法确定ECoG为混沌时间序列 ,从不同角度得出了ECoG不是低维混沌的结论 .在ECoG相空间重构的基础上 ,计算了最大Lyapunov指数 (LLE) .应用了近似熵这一标量对ECoG进行刻画 ,计算结果表明 :癫痫发作前的皮层脑电的最大Lyapunov指数和近似熵都明显地高于癫痫发作后的 ,这可能为理解癫痫发病机理 ,预报癫痫发作和治疗提供一定的思路 .
The attractors, obtained from the ECoG time series of anaesthetized SD rat before and after epileptic seizure, are reconstructed first by making use of time-delay coordinates. Many efficient approaches and analysis techniques are applied to the ECoG series, thus the ECoG series are exactly analyzed. Consequently, a constructive result is obtained. Through calculating mutual information function, its first local minimum is defined as the time delay. And the method of uniting false nearest neighbour with Cao method is used to determine the optimal minimum embedding dimension. And then the ECoG sequences are considered as a chaotic series combining nonlinear prediction with surrogate data method, and are educed to be not a low-dimensional chaos. The largest Lyapunov exponents are computed on the basis of phase-space reconstruction of ECoG, at the same time the approximate entropies are calculated. The computational results show that there are distinct differences in the largest Lyapunov exponents and the approximate entropies before and after epileptic onset, which can provide with some clues for understanding the mechanism of epilepsy and predicting epileptic seizure and curing epileptic patients.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2002年第2期205-214,共10页
Acta Physica Sinica
基金
国家自然科学基金 (批准号 :30 0 30 0 40和 19972 0 5 1)资助的课题~~
