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首页> 外文期刊>Biomedical signal processing and control >Classification of inter-ictal and ictal EEGs using multi-basis MODWPT, dimensionality reduction algorithms and LS-SVM: A comparative study
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Classification of inter-ictal and ictal EEGs using multi-basis MODWPT, dimensionality reduction algorithms and LS-SVM: A comparative study

机译：使用多基MODWPT，降维算法和LS-SVM对发作间和发作间脑电图进行分类的比较研究

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Wavelet-based feature extraction techniques are very promising for classifying epileptic electroencephalograph (EEG) but the determination of the optimal wavelet basis is intractable. To overcome this strait, maximal overlap discrete wavelet package transform (MODWPT) was introduced to characterize epileptic EEG for the first time and the multi-basis MODWPT-based feature extraction was further proposed in this study. Instead of merely using single wavelet basis, multiple bases were synchronously adopted in one-time implementation of multi-basis MODWPT. Six dimensionality reduction algorithms, namely principal component analysis (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA), isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE), were then employed to map the extracted features into other domain in feature selection procedure. Finally, the mapped features were fed into a least squares support vector machine (LS-SVM) for classification and multiple kernel learning support vector machine (MKLSVM) was used as comparison. Experimental results show the combination of multi-basis MODWPT, ICA and LS-SVM with linear kernel provides the highest accuracy of 99.67% in classifying inter-ictal and ictal EEGs while MKLSVM in conjunction with multi-basis MODWPT-based PCA also leads to the maximal accuracy of 99.83%. Our presented scheme yields superior performance than the majority of newly-developed methods and is proven useful. (C) 2018 Elsevier Ltd. All rights reserved.
机译：基于小波的特征提取技术对于癫痫脑电图（EEG）的分类非常有前途，但是确定最佳小波基础是很棘手的。为了克服这一困难，首次引入最大重叠离散小波包变换（MODWPT）表征癫痫脑电图，并在此基础上进一步提出了基于多基MODWPT的特征提取。在一次性实现多基MODWPT的过程中，同步采用了多个基，而不仅仅是使用单个小波基。然后采用了六维降维算法，分别是主成分分析（PCA），独立成分分析（ICA），内核主成分分析（KPCA），等距特征映射（ISOMAP），局部线性嵌入（LLE）和拉普拉斯特征图（LE）用于在特征选择过程中将提取的特征映射到其他域。最后，将映射的特征馈入最小二乘支持向量机（LS-SVM）进行分类，并使用多核学习支持向量机（MKLSVM）作为比较。实验结果表明，多基MODWPT，ICA和LS-SVM与线性核的组合在对眼间和脑电脑电图进行分类时可提供99.67％的最高准确度，而MKLSVM与基于多基MODWPT的PCA结合也可导致最高准确度为99.83％。我们提出的方案比大多数新开发的方法具有更高的性能，并被证明是有用的。 （C）2018 Elsevier Ltd.保留所有权利。

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