Seizure Prediction using Convolutional Neural Networks and Sequence Transformer Networks

Accurate seizure prediction is important for design of wearable and implantable devices that can improve the lives of subjects with epilepsy. Such implantable devices can be used for closed-loop neuromodulation. However, there are many challenges that inhibit the performance of prediction models. One challenge in accurately predicting seizures is the nonstationarity of the EEG signals. This paper presents a patient-specific deep learning approach to improve predictive performance by transforming EEG data before extracting features for seizure prediction. In the proposed approach, a Sequence Transformer Network (STN) is first used to learn temporal and magnitude invariances in EEG data. The proposed method further computes the short-time Fourier transform (STFT) of the transformed EEG signals as input features to a convolutional neural network (CNN). A k-out-of-n post-processing method is used to reduce the significance of isolated false positives. The approach is tested using intracranial EEG from the American Epilepsy Society Seizure Prediction Challenge dataset. Leave-one-out cross validation is used to evaluate the model. The proposed model achieves an overall sensitivity of 82%, false prediction rate of 0.38/hour, and average AUC of 0.746.