Immune clonal algorithm based feature selection for epileptic EEG signal classification
Immune clonal algorithm based feature selection for epileptic EEG signal classification
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DOI：
10.1109/isspa.2012.6310672
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发表时间：
2012-07
期刊：
影响因子：
--
通讯作者：
Yong Peng;Bao-Liang Lu
中科院分区：
文献类型：
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作者：
Yong Peng;Bao-Liang Lu
自动准确地检测癫痫脑电信号对癫痫患者的病情评估具有重要意义。在这项研究中，免疫克隆算法（伊卡）进行自动特征选择，减少了分类器处理的特征数量，提高了分类精度。在实验中，脑电信号被分解成五个子带分量的离散小波变换。提取特征作为输入，训练三个分类器（NB，SVM，KNN和LDA）来判断脑电信号是否为癫痫。然后，伊卡被引入到选择一个特征子集来训练分类器。实验结果表明，基于选择特征的分类精度明显高于基于原始特征的分类精度。我们还分析了每个特征的相对重要性。
Detecting epileptic EEG signal automatically and accurately is significant in evaluating patients with epilepsy. In this study, the immune clonal algorithm (ICA) is employed to perform automatic feature selection, reducing the number of features the classifier deals with and improving the classification accuracy. In the experiment, EEG signal was decomposed into five sub-band components by a discrete wavelet transform. Features were extracted as input to train three classifiers (NB, SVM, KNN and LDA) to judge whether the EEG signal was epileptic or not. Then, ICA was introduced to select a feature subset to train the classifiers. Experimental results show that the classification accuracy based on selected features is significantly higher than that on original features. We also analyzed the relative importance of each feature.