Epilepsy is a neurological disease from which a large number of younger and older people suffer all over the world. The status of the patients is primarily examined by using Electroencephalogram (EEG) signals. The most important part for successful surgery is to locate the epileptic seizure in the brain. For this reason, it is very useful to detect the seizure area automatically before surgery. In this research, a novel method based on continuous wavelet transform (CWT) and two-dimensional (2D) convolutional neural networks (CNNs) has been proposed to predict focal and non-focal epileptic seizure. The AlexNet, InceptionV3, Inception-ResNetV2, ResNet50 and VGG16 pre-trained models have been used to automatically classify 2D-scalogram images into focal and non-focal epileptic seizure. The performances of 5 pre-trained models were compared and the detection results of 2D-scalograms were examined. The best classification accuracy of 92.27% is yielded by the InceptionV3 model among the other used four pre-trained models. As a result, it may be said that the pre-trained models and 2D-scalogram images of focal and non-focal EEG signals will be useful to neurologists for rapid and robust prediction epileptic seizure before surgery.

癫痫是一种神经系统疾病，全世界大量年轻人和老年人都患有这种疾病。主要通过使用脑电图 (EEG) 信号检查患者的状态。成功手术最重要的部分是在大脑中定位癫痫发作。因此，在手术前自动检测癫痫发作区域非常有用。在这项研究中，提出了一种基于连续小波变换 (CWT) 和二维 (2D) 卷积神经网络 (CNN) 的新方法来预测局灶性和非局灶性癫痫发作。AlexNet、InceptionV3、Inception-ResNetV2、ResNet50 和 VGG16 预训练模型已用于自动将 2D 尺度图图像分类为局灶性和非局灶性癫痫发作。比较了 5 个预训练模型的性能，并检查了 2D 尺度图的检测结果。在其他使用的四个预训练模型中，InceptionV3 模型产生了 92.27% 的最佳分类准确率。因此，可以说，局灶和非局灶 EEG 信号的预训练模型和 2D 尺度图图像将有助于神经科医生在手术前快速和稳健地预测癫痫发作。