We present and evaluate the capacity of a deep neural network to learn robust
features from EEG to automatically detect seizures. This is a challenging
problem because seizure manifestations on EEG are extremely variable both
inter- and intra-patient. By simultaneously capturing spectral, temporal and
spatial information our recurrent convolutional neural network learns a general
spatially invariant representation of a seizure. The proposed approach exceeds
significantly previous results obtained on cross-patient classifiers both in
terms of sensitivity and false positive rate. Furthermore, our model proves to
be robust to missing channel and variable electrode montage.

本研究提出并评估了深度神经网络从 EEG 中学习稳健特征以自动检测癫痫的能力，通过同时捕获频谱、时间和空间信息，我们的循环卷积神经网络学习了癫痫的空间不变表示。该方法在交叉患者分类器上的结果显著优于先前的结果，具有较高的灵敏度和低的假阳性率。此外，我们的模型对于缺失通道和不同的电极配置也表现出稳健性。