  An accurate seizure prediction system enables early warnings before seizure
onset of epileptic patients. It is extremely important for drug-refractory
patients. Conventional seizure prediction works usually rely on features
extracted from Electroencephalography (EEG) recordings and classification
algorithms such as regression or support vector machine (SVM) to locate the
short time before seizure onset. However, such methods cannot achieve
high-accuracy prediction due to information loss of the hand-crafted features
and the limited classification ability of regression and SVM algorithms. We
propose an end-to-end deep learning solution using a convolutional neural
network (CNN) in this paper. One and two dimensional kernels are adopted in the
early- and late-stage convolution and max-pooling layers, respectively. The
proposed CNN model is evaluated on Kaggle intracranial and CHB-MIT scalp EEG
datasets. Overall sensitivity, false prediction rate, and area under receiver
operating characteristic curve reaches 93.5%, 0.063/h, 0.981 and 98.8%,
0.074/h, 0.988 on two datasets respectively. Comparison with state-of-the-art
works indicates that the proposed model achieves exceeding prediction
performance.
