Non linear analysis of epileptic EEG

J. E. Jacob, V. Vijith, K. Gopakumar
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引用次数: 4

Abstract

EEG contains immense information about the brain activity which cannot be understood completely by visual inspection. Powerful signal processing algorithms in EEG analysis can greatly assist the physicians and neurologists to extract such hidden information. It has been found that EEG being a time-varying and non-stationary signal, can be analyzed by non-linear methods. In this paper we tried to evaluate the non linear features, Correlation dimension, Approximate Entropy, Sample Entropy, and Hurst Exponent in epileptic and normal EEG and has obtained clear discrimination between them.
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癫痫脑电图的非线性分析
脑电图包含了大量关于大脑活动的信息,这些信息是肉眼无法完全理解的。脑电图分析中强大的信号处理算法可以极大地帮助医生和神经科医生提取这些隐藏信息。研究发现,脑电信号是时变的非平稳信号,可以用非线性方法进行分析。本文对癫痫和正常脑电图的非线性特征、相关维数、近似熵、样本熵和赫斯特指数进行了评价,得到了两者的明显区别。
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