The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. This study describes an automated classification of EEG signal for the detection of Epilepsy disease using soft computing methods. The proposed method is comprised of three modules: (a) transformation, (b) feature computation, and (c) feature classifications. In the first module, the nonsubsampled contourlet transform is applied on the EEG signal which decomposes the signal into approximate and directional subbands. The decomposition is done using nonsubsampled pyramid filter bank and nonsubsampled directional filter bank respectively. Secondly, the statistical features are extracted from the decomposed directional subbands using wavelet packet decomposition method. Finally, these features are classified by adaptive neuro‐Fuzzy inference system classification method, which classifies the EEG signal into either focal or nonfocal signal. The proposed method is tested on a set of EEG signals for validation. The average classification rate of the proposed EEG signal classification system is 99.4%. The proposed EEG signal classification methodology achieves a sensitivity of 99.7%, a specificity of 99.7%, and an accuracy of 99.4%. The results confirmed that the proposed method has a potential in the classification of EEG signals and thereby could further improve the diagnosis of epilepsy.

中文翻译：

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使用机器学习方法对癫痫病的脑电图信号进行检测和分类

脑电图（EEG）信号在癫痫的诊断中起关键作用。这项研究描述了使用软计算方法对脑电信号进行自动分类以检测癫痫病的方法。所提出的方法包括三个模块：（a）变换，（b）特征计算和（c）特征分类。在第一个模块中，对EEG信号进行非下采样Contourlet变换，该变换将信号分解为近似和定向子带。分别使用非下采样的金字塔滤波器组和非下采样的定向滤波器组进行分解。其次，利用小波包分解方法从分解后的方向子带中提取统计特征。最后，这些特征通过自适应神经模糊推理系统分类方法进行分类，该方法将EEG信号分为聚焦信号或非聚焦信号。在一组EEG信号上测试了所提出的方法以进行验证。提出的脑电信号分类系统的平均分类率为99.4％。提出的EEG信号分类方法可实现99.7％的灵敏度，99.7％的特异性和99.4％的准确度。结果证实了所提出的方法在脑电信号分类中具有潜力，从而可以进一步改善癫痫的诊断。提出的EEG信号分类方法可实现99.7％的灵敏度，99.7％的特异性和99.4％的准确度。结果证实了所提出的方法在脑电信号分类中具有潜力，从而可以进一步改善癫痫的诊断。提出的EEG信号分类方法可实现99.7％的灵敏度，99.7％的特异性和99.4％的准确度。结果证实了所提出的方法在脑电信号分类中具有潜力，从而可以进一步改善癫痫的诊断。