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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification
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Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification

机译：设计基于最优分配和主成分分析的鲁棒特征提取方法用于癫痫脑电信号分类

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The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA samples for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：这项研究的目的是设计一种可靠的特征提取方法，用于对多类EEG信号进行分类，从而从原始癫痫EEG数据中确定有价值的特征，并发现这些特征的有效分类器。开发了一种基于最优分配的主成分分析方法OA_PCA，用于从癫痫EEG数据中提取特征。由于来自不同渠道的EEG数据相互关联且数量巨大，因此使用最佳分配（OA）方案从大量EEG数据中发现变异性最小的最有利代表。主成分分析（PCA）用于构建不相关的成分，还可以减小OA样本的维数以增强识别能力。为了为OA_PCA功能集选择合适的分类器，共有四个流行的分类器：最小二乘支持向量机（LS-SVM），朴素贝叶斯分类器（NB），k最近邻算法（KNN）和线性判别分析（LDA） ）进行测试。此外，我们的方法还与最近的一些研究工作进行了比较。实验结果表明，LS-SVM_1v1方法可产生100％的整体分类精度（OCA），比现有的癫痫EEG数据算法提高了7.10％。这项研究的主要发现是，具有1v1系统的LS-SVM是癫痫EEG信号分类中OA_PCA功能的最佳技术，其性能优于文献中所有最近报道的现有方法。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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