Application of approximate entropy on dynamic characteristics of epileptic absence seizure

查看全文 作　　者：Yi [1,2]Zhou;Ruimei [1]Huang;Ziyi [3]Chen;Xin [1]Chang;Jialong [1]Chen;Lingli [4]Xie

机构地区：[1]Department of Biomedical Engineering,Zhongshan School of Medicine,Sun Yat-sen University,Guangzhou 510080,Guangdong Province,China;[2]School of Biomedical Engineering and Technology,Xinjiang Medical University,Urumqi 830011,Xinjiang Uygur Autonomous Region,China;[3]Department of Neurology,the First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,Guangdong Province,China;[4]Department of Mathematics,Sun Yat-sen University,Guangzhou 510275,Guangdong Province,China

出　　处：《Neural Regeneration Research》索引2012年第7卷第8期,共6页

基　　金：supported by the National Natural Science Foundation of China, No.10671213 and 11101440;the Natural Science Foundation of Guangdong Province;Fundamental Research Funds for the Central Universities 摘　　要：Electroencephalogram signals are time-varying complex electrophysiological signals. Existing studies show that approximate entropy, which is a nonlinear dynamics index, is not an ideal method for electroencephalogram analysis. Clinical electroencephalogram measurements usually contain electrical interference signals, creating additional challenges in terms of maintaining robustness of the analytic methods. There is an urgent need for a novel method of nonlinear dynamical analysis of the electroencephalogram that can characterize seizure-related changes in cerebral dynamics. The aim of this paper was to study the fluctuations of approximate entropy in preictal, ictal, and postictal electroencephalogram signals from a patient with absence seizures, and to improve the algorithm used to calculate the approximate entropy. The approximate entropy algorithm, especially our modified version, could accurately describe the dynamical changes of the brain during absence seizures. We could also demonstrate that the complexity of the brain was greater in the normal state than in the ictal state. The fluctuations of the approximate entropy before epileptic seizures observed in this study can form a good basis for further study on the prediction of seizures with nonlinear dynamics. 关键词：近似熵动态特性癫痫非线性动力学分析电生理信号应用脑电图动力学指数

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Application of approximate entropy on dynamic characteristics of epileptic absence seizure

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