  In this article, we propose an approach that can make use of not only labeled
EEG signals but also the unlabeled ones which is more accessible. We also
suggest the use of data fusion to further improve the seizure prediction
accuracy. Data fusion in our vision includes EEG signals, cardiogram signals,
body temperature and time. We use the short-time Fourier transform on 28-s EEG
windows as a pre-processing step. A generative adversarial network (GAN) is
trained in an unsupervised manner where information of seizure onset is
disregarded. The trained Discriminator of the GAN is then used as feature
extractor. Features generated by the feature extractor are classified by two
fully-connected layers (can be replaced by any classifier) for the labeled EEG
signals. This semi-supervised seizure prediction method achieves area under the
operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp
EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively.
Unsupervised training without the need of labeling is important because not
only it can be performed in real-time during EEG signal recording, but also it
does not require feature engineering effort for each patient.
