摘要
为提取癫痫发作与间歇期脑电信号的特征,提出利用构建癫痫EEG(electroencephalogram)网络的方法来刻画脑电信号。研究各变量均可测情况下的Lorenz和R9ssler混沌系统,利用其各变量的输出混沌时间序列构建复杂网络,发现构建的复杂网络拓扑图与其混沌吸引子存在形态相似性,说明由时间序列构建的复杂网络能刻画其原信号特征。对于多维系统中仅有一维可测时,多维时间序列由相空间重构得到。利用相空间重构方法对癫痫发作和间歇期脑电信号构建复杂网络进行分析。研究结果表明,癫痫发作时其网络拓扑较间歇期存在明显不同,且其平均路径长度显著增加,而递归率及其波动范围都显著降低,这些网络特性可以用来刻画脑电信号的特征,从而为癫痫疾病的自动辨识与预测提供基础。
To extract epileptic EEG features in the ictal and interictal period, a method of depicting epileptic EEG was proposed by transforming epileptic EEG time series to epileptic networks. Chaotic multi-dimensional time series coming from the Lorenz system and Rtissler system were used to construct a complex network, in which all the variables could be measured. It was found that there was morphological similarity between topology of the complex networks and the at- tractor of chaotic system. This indicated that complex networks constructed from time series could depict the characteris- tics of the original signals. For only one measureable variable, multi-dimensional time series were obtained by recon- struction of the phase space. Therefore, the epileptic EEG network was constructed and analyzed in the ictal and interic- tal period. The results showed that epileptic EEG network topologies in the ictal period were significantly different from that in the interictal period. Meanwhile, the average path length of the network increased significantly and recurrence rates decreased significantly in the ictal period comparing to in the interictal period. These network features could be used to depict the characteristics of EEG time series and could provide the basis for epilepsy automatic identification and prediction.
作者
郝崇清
王志宏
HAO Chongqing WANG Zhihong(School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, Hebei, China School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China)
出处
《山东大学学报（工学版）》
CAS
北大核心
2017年第3期8-15,共8页
Journal of Shandong University（Engineering Science）
基金
河北省自然科学基金资助项目(F2014208013)
关键词
复杂网络
癫痫脑电
网络拓扑
形态相似性
平均路径长度
递归率
complex networks
epileptic EEG
network topology
morphological similarity
average path length
re- currence rates
