Entropies for detection of epilepsy in EEG

被引:566
作者
Kannathal, N [1 ]
Choo, ML
Acharya, UR
Sadasivan, PK
机构
[1] Natl Univ Singapore, Dept ECE, Singapore 119260, Singapore
[2] NgeeAnn Polytech, Div Elect & Comp Engn, Singapore 599489, Singapore
关键词
electroencephalogram; epilepsy; Kotmogorov entropy; spectral entropy; Renyi entropy; approximate entropy; ANFIS classifier;
D O I
10.1016/j.cmpb.2005.06.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. The aim of this work is to compare the different entropy estimators when applied to EEG data from normal and epileptic subjects. The results obtained indicate that entropy estimators can distinguish normal and epileptic EEG data with more than 95% confidence (using t-test). The classification ability of the entropy measures is tested using ANFIS classifier. The results are promising and a classification accuracy of about 90% is achieved. (c) 2005 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:187 / 194
页数:8
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