Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.

中文翻译：

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基于深度卷积神经网络的癫痫脑电图（EEG）信号分类。

脑电图（EEG）信号包含有关大脑电活动的重要信息，被广泛用于辅助癫痫病分析。癫痫诊断的一个挑战性要素，即不同癫痫状态的准确分类，引起了人们的特别关注，并且已得到广泛研究。本文提出了一种新的基于深度学习的分类方法，即癫痫脑电信号分类（EESC）。该方法首先将癫痫性脑电信号转换为功率谱密度能图（PSDED），然后应用深度卷积神经网络（DCNN）并进行转移学习以从PSDED中自动提取特征，最后对癫痫状态的四类进行分类（发作期，发作期）至30分钟，发作前持续10分钟，然后发作）。就准确性和效率而言，它优于现有的癫痫分类方法。例如，在使用CHB-MIT癫痫性脑电图数据进行的案例研究中，该方法可实现90％以上的平均分类精度。