This work investigates EEG signal processing and seizure prediction based on deep learning architectures. The research includes two major parts. In the first part, we use wavelet decomposition to process the signals and extract signal features from the time-frequency bands. The second part examines the machine learning model and deep learning architecture we have developed for seizure pattern analysis. In our design, the extracted feature maps are processed as image inputs into our convolutional neural network (CNN) model. We proposed a combined CNN-LSTM model to directly process the EEG signals with layers functioning as feature extractors. In cross-validation testing, our CNN feature model can reach an accuracy of 96% and our CNN-LSTM model could reach an accuracy of 98%. We also proposed a matching network architecture that employs two parallel multilayer channels to improve sensitivity.