Patient-specific Seizure Prediction with Scalp EEG Using Convolutional Neural Network and Extreme Learning Machine

Seizure prediction can allow timely preventive measures for patients with epilepsy. In this study, we propose a hybrid model consisting of convolutional neural networks (CNNs) and an extreme learning machine (ELM) to predict seizures using scalp EEG. We first covert the EEG time series on 30-s windows into 2D spectrograms using the short-time Fourier transform. Then we apply CNNs to these images to extract features automatically. Finally, we use the ELM to classify preictal and interictal segments. The proposed method achieves sensitivity of 95.85% and a false prediction rate of 0.045/h on the Boston Children's Hospital-MIT scalp EEG dataset.