The uncertainty in human brain leads to the formation of epilepsy disease in human. The automatic detection and severity analysis of epilepsy disease is proposed in this article using a hybrid classification algorithm. The proposed method consists of decomposition stage, feature extraction, and classification stages. The electroencephalogram (EEG) signals are decomposed using dual‐tree complex wavelet transform and then features are extracted from these coefficients. These features are then classified using the neural network classification approach in order to classify the EEG signals into either focal or nonfocal EEG signals. Furthermore, severity of the focal EEG signal is analyzed using an adaptive neuro‐fuzzy inference system classification approach. The proposed hybrid classification method for the classification of focal signals and nonfocal signals achieved 98.6% of sensitivity, 99.1% of specificity, and 99.4% of accuracy. The average detection rate for both focal and nonfocal dataset is about 98.5%.

中文翻译：

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一种使用混合分类方法对局灶性和非局灶性 EEG 信号进行分类的自动化方法

人脑的不确定性导致了人类癫痫病的形成。本文使用混合分类算法提出了癫痫病的自动检测和严重程度分析。所提出的方法由分解阶段、特征提取和分类阶段组成。使用双树复小波变换分解脑电图 (EEG) 信号，然后从这些系数中提取特征。然后使用神经网络分类方法对这些特征进行分类，以便将 EEG 信号分类为焦点或非焦点 EEG 信号。此外，使用自适应神经模糊推理系统分类方法分析局灶 EEG 信号的严重性。所提出的用于对局灶信号和非局灶信号进行分类的混合分类方法实现了 98.6% 的灵敏度、99.1% 的特异性和 99.4% 的准确度。焦点和非焦点数据集的平均检测率约为 98.5%。