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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。
In order to extract the characteristics of epileptic seizures and intermittent EEG signals, this paper proposes to use EEG (electroencephalogram) network to describe EEG signals. The Lorenz and R9ssler chaotic systems are tested under variable conditions. The chaotic time series of the output variables of each variable are used to construct the complex networks. It is found that there are morphological similarities between the constructed complex network topologies and their chaotic attractors, Complex networks can portray their original signal characteristics. For only one-dimensional measurable in multi-dimensional system, multi-dimensional time series are obtained by phase space reconstruction. Using phase space reconstruction method to analyze the complex networks of epileptic seizures and intermittent EEG signals. The results show that the network topology of epileptic seizures is significantly different from the intermittent period, and the average path length is significantly increased, while the recursion rate and its fluctuation range are significantly reduced, these network characteristics can be used to characterize the EEG signals Provide the basis for the automatic identification and prediction of epilepsy diseases.