Epilepsy is one of the most common neurological diseases of the human brain. It affects the nervous system of brain which shows the impact on an individual's life because of its repetitious occurrences of seizure. Epileptic detection using automatic learning is essential to reduce the substantial work on reviewing continuous electroencephalogram (EEG) signal in spatial and temporal dimensions. A novel methodology is implemented on EEG signals for the detection of epileptic seizure with the combination of fractional S‐transform (FST) and entropies along with deep convolutional neural networks (CNN). The original EEG signals are preprocessed with discrete wavelet transform to generate Daubechies‐4 (Db4) wavelets. FST is enacted on every segment of the preprocessed signal for time‐frequency representation and the features are obtained through entropies. Afterwards, a 15‐layer deep CNN with dropout layer and soft‐max is used for classification. The experimental results showed that the singular value decomposition entropy are more stable and deep CNN models always performed better for this entropy. A specificity of 98.70%, sensitivity of 97.71%, and accuracy of 99.70% are achieved for the multichannel segment.

中文翻译：

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利用分数S变换对癫痫发作进行检测的深层神经网络用于对脑电图信号进行分类

癫痫病是人脑最常见的神经系统疾病之一。它会影响大脑的神经系统，因为它会反复发作，对个人的生活产生影响。使用自动学习的癫痫检测对于减少在空间和时间维度上检查连续脑电图（EEG）信号的实质性工作至关重要。通过分数S变换（FST）和熵以及深度卷积神经网络（CNN）的组合，对脑电信号实施了一种新颖的方法来检测癫痫发作。使用离散小波变换对原始EEG信号进行预处理，以生成Daubechies-4（Db4）小波。在预处理信号的每个片段上执行FST以进行时频表示，并通过熵获得特征。然后，使用15层深的CNN（具有辍学层和soft-max）进行分类。实验结果表明，奇异值分解熵更稳定，并且深CNN模型始终对此熵表现更好。多通道片段的特异性为98.70％，灵敏度为97.71％，准确度为99.70％。