INTRODUCTION The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. METHODS In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. RESULTS The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. DISCUSSION/CONCLUSION The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

中文翻译：

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使用预训练的深度卷积神经网络和迁移学习检测癫痫发作

引言 癫痫的诊断需要一定的过程，完全取决于主治医师。然而，人为因素可能导致在分析EEG信号时出现错误诊断。在过去的 20 年中，已经开发了许多先进的信号处理和机器学习方法来检测癫痫发作。然而，许多这些方法需要大数据集和复杂的操作。方法 在这项研究中，提出了一种端到端机器学习模型，用于使用预训练的深度二维卷积神经网络 (CNN) 和迁移学习的概念来检测癫痫发作。EEG 信号通过频谱图直接转换为视觉数据，并直接用作输入数据。结果作者分析了所提出的预训练 AlexNet CNN 模型的训练结果。二元和三元分类都是在没有任何额外程序（例如特征提取）的情况下进行的。通过从短期频谱图图像创建数据集，作者能够实现 100% 的癫痫发作检测二元分类准确率和 100% 的三元分类准确率。讨论/结论所提出的自动识别和分类模型可以帮助癫痫的早期诊断，从而为有效的早期治疗提供机会。