To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

自动检测动态脑电信号，以减少癫痫诊断的时间成本。在癫痫病的脑电图（EEG）信号识别中，传统的机器学习和统计方法需要手动特征标记工程，以便在单个数据集上显示出色的结果。而且，人为选择的特征可能带有偏差，并且不能保证真实数据的有效性和可扩展性。在实际应用中，深度学习方法可以在一定程度上使人们脱离要素工程。只要关注数据质量和数量的扩展，算法模型就可以自动学习以获得更好的改进。另外，深度学习方法还可以提取人类难以感知的许多特征，从而使算法更加健壮。基于ResNeXt深层神经网络的设计思想，设计了一种适用于时间序列EEG癫痫检测以识别EEG信号的Time-ResNeXt网络结构。Time-ResNeXt检测脑电图癫痫的准确率可达91.50％。Time-ResNeXt网络结构在基准数据集（Berne-Barcelona数据集）上产生了极其先进的性能，并具有改善临床实践的巨大潜力。