Semi-dilated convolutional neural networks for epileptic seizure prediction

Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephalogram (EEG) is challenging. Classical machine learning algorithms and a variety of feature engineering methods have become a mainstay in seizure prediction, yet performance has been variable. In this work, we first propose an efficient data pre-processing method that maps the time-series EEG signals into an image-like format (a scalogram) using continuous wavelet transform. We then develop a novel module named convolution that better exploits the geometry of wavelet scalograms and nonsquare-shape images. Finally, we propose a neural network architecture named convolutional network (SDCN) that uses semi-dilated convolutions to solely expand the receptive field along the long dimension (image width) while maintaining high resolution along the short dimension (image height). Results demonstrate that the proposed SDCN architecture outperforms previous seizure prediction methods, achieving an average seizure prediction sensitivity of 98.90% for scalp EEG and 88.45-89.52% for invasive EEG.