Decision support system for focal EEG signals using tunable-Q wavelet transform
Decision support system for focal EEG signals using tunable-Q wavelet transform
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DOI：
10.1016/j.jocs.2017.03.022
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发表时间：
2017-05-01
影响因子：
3.3
通讯作者：
Acharya, U. Rajendra
中科院分区：
文献类型：
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作者：
Sharma, Rajeev;Kumar, Mohit;Acharya, U. Rajendra
在目前的工作中，我们提出了一种自动识别局灶脑电信号的系统。在可调Q小波变换(Tqwt)框架下，对聚焦(F)和非聚焦(NF)脑电信号中存在的非线性进行了量化。首先，利用Tqwt将两类脑电信号分解成不同的子带。从这些子带计算了不同的非线性特征，即K-最近邻熵估计器(KnnEnt)、中心相关熵(CcorEnt)、模糊熵(FzEnt)、双谱熵、排列熵(PmEnt)、样本熵(Sment)、分维(FracDm)和最大Lyapunov指数(LLE)。这些特征揭示了F和NFEEG信号各个子带的复杂性。最小二乘支持向量机(LS-SVM)分类器仅使用KnnEnt特征，分类正确率最高，达到94.06%。在最小二乘支持向量机分类器中，采用KnnEnt、CcorEnt和FzEnt三种熵的分类结果提高到95.00%。在F类和NF类分类中，我们获得了最高的分类性能，可以用来准确地定位局灶性癫痫患者的手术区域。(C)2017爱思唯尔B.V.保留所有权利。
In the present work, we have proposed an automated system to identify focal electroencephalogram (EEG) signals. The nonlinearity present in the focal (F) and non-focal (NF) EEG signals is quantified in tunable-Q wavelet transform (TQWT) framework. First, the EEG signals of both classes are decomposed into different subbands using TQWT. Different nonlinear features namely, K-nearest neighbour entropy estimator (KnnEnt), centered correntropy (CCorrEnt), and fuzzy entropy (FzEnt), bispectral entropies, permutation entropy (PmEnt), sample entropy (SmEnt), fractal dimension (FracDm) and largest Lyapunov exponent (LLE) are computed from these subbands. These features reveal the complexity present in various subbands of F and NF EEG signals. Our proposed method showed highest classification accuracy of 94.06% with least squares-support vector machine (LS-SVM) classifier using only KnnEnt features. The results of classification increased to 95.00% using three entropies (KnnEnt, CCorrEnt, and FzEnt) with LS-SVM classifier. We have obtained the highest classification performance in the classification of F and NF classes which can be used to locate the region of surgery in focal epileptic patients accurately. (C) 2017 Elsevier B.V. All rights reserved.