Semi-Supervised Seizure Prediction Based on Deep Pairwise Representation Alignment of Epileptic EEG Signals

Electroencephalogram (EEG) signals are electrical signals generated by the activity between neurons in the brain and are already extensively applied for seizure prediction. Semi-supervised learning (SSL) has been applied in seizure prediction studies, as acquiring labeled EEG data can be time-consuming and costly. Current available SSL methods use far fewer labeled EEG samples than unlabeled EEG samples to train models. However, these methods have two limitations. First, the scarcity of EEG data limits the generalizability of the model. Second, the imbalance in the numbers of labeled and unlabeled EEG samples during model training leads to a potential distribution mismatch between the two sample types. Therefore, a novel semi-supervised seizure prediction method is designed in this study. First, an EEG data augmentation network is used to augment EEG signals at the signal level. Then, the labels of the original and augmented unlabeled EEG data are acquired from the spectrograms of EEG signals. Subsequently, the acquired labels are sharpened and the convex combination of labeled and unlabeled data is computed. Finally, deep pairwise representation alignment is performed to predict seizures. Several experiments are conducted using the proposed method on the Children's Hospital Boston-Massachusetts Institute of Technology EEG database and the Kaggle Epilepsy Prediction Challenge EEG database. The results show that the proposed semi-supervised seizure prediction method is effective and reliable, given that it provides satisfactory results with as few as 25 labeled data per class when trained on these two datasets.