Analysis of Epileptic and Normal EEG Signals based on Random Walk

被引：0

作者：

Min, Jun ^{[1
]}

Wang, Jun ^{[2
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Sch Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210003, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE 3RD ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2017) | 2017年 / 131卷

基金：

中国国家自然科学基金;

关键词：

random walk; Bayesian inference; epilepsy; EEG signal;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, a random walk model was established for the EEG signal. The characteristic parameters qt and at were extracted from the original EEG signal by Bayesian inference method. By comparing the global maximum of the autocorrelation function of the characteristic parameter qt between patients with epilepsy and normal subjects, this paper obtained the following conclusion, the global maximum of the autocorrelation function of the characteristic parameter qt of EEG signal extracted in epilepsy patients is larger than the normal human in the general trend; and the fluctuation of the global maximum of the autocorrelation function in epilepsy patients is also greater, which suggests that the use of random walk model to analyze the EEG signal can be found the difference in patients with epilepsy and normal subjects; therefore, random walk model can be used to analyze the difference between epileptic EEG signal and normal human EEG signals.

引用

页码：379 / 384

页数：6

共 7 条

[1]

[Anonymous], 2013, PRINCIPLES RANDOM WA

[2]

Box GEP, 1992, BAYESIAN INFERENCE S, P478

[3]

Dliou A., 2012, INT J COMPUTER APPL, V41

[4] Independent component analysis as a tool to eliminate artifacts in EEG: A quantitative study [J].