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首页> 外文期刊>Biomedical signal processing and control >Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking
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Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking

机译：使用集成叠加对VMD-DWT域中的聚焦和非聚焦EEG信号进行分类

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摘要

Classification of focal and non-focal Electroencephalogram (EEG) signals is an important problem especially for the identification of epileptogenic sites in the brain. However, the number of research works reported in the literature is limited and most of them suffer from validation on a limited scale and moderate accuracy. In this paper, focal and non-focal EEG signals are analyzed in variational mode decomposition (VMD) and discrete wavelet transform (DWT) domain and features such as refined composite multiscale dispersion entropy, refined composite multiscale fuzzy entropy, and autoregressive model (AR) coefficients are extracted in VMD, DWT and VMD-DWT domain. Statistical analysis of the features is carried out by one way ANOVA to demonstrate their discriminating ability by means of p-values and visual inspection of the box plots of the features. A feature reduction algorithm based on neighborhood component analysis is used to reduce the model complexity and select the features with the highest discriminating abilities. An ensemble stacking classification approach is adopted to improve the accuracy of classification. The performance of the proposed method is studied using a publicly available benchmark database that contains 3750 pairs of focal and 3750 pairs of non-focal EEG signals. It is shown that the stacking configuration improves the accuracy significantly compared to a standalone classifier. It gives high values of sensitivity (96.1%), Specificity (94.4%), Accuracy (95.2%) and area under curve (0.989). Comparison with various existing methods reveals that the proposed method outperforms the others in accuracy. It may help researchers in developing a computer-aided system to identify epileptogenic sites in the brain. (C) 2019 Elsevier Ltd. All rights reserved.
机译：局灶性和非局灶性脑电图（EEG）信号的分类是一个重要的问题，尤其是对于识别大脑中的癫痫发生部位。但是，文献中报道的研究工作数量有限，并且大多数受到有限规模和中等准确性的验证。本文在变分模式分解（VMD）和离散小波变换（DWT）域中分析了聚焦和非聚焦EEG信号以及诸如精细复合多尺度色散熵，精细复合多尺度模糊熵和自回归模型（AR）之类的特征在VMD，DWT和VMD-DWT域中提取系数。通过一种方差分析对特征进行统计分析，以通过p值和对特征箱形图的目测来证明其区分能力。使用基于邻域成分分析的特征约简算法来降低模型复杂度并选择具有最高识别能力的特征。采用集成堆叠分类方法来提高分类的准确性。使用包含3750对焦点和3750对非焦点EEG信号的公开基准数据库研究了所提出方法的性能。结果表明，与独立分类器相比，堆叠配置显着提高了准确性。它提供了很高的灵敏度（96.1％），特异性（94.4％），准确性（95.2％）和曲线下面积（0.989）值。与各种现有方法的比较表明，所提方法的准确性优于其他方法。它可以帮助研究人员开发计算机辅助系统，以识别大脑中的致癫痫部位。 （C）2019 Elsevier Ltd.保留所有权利。

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