A Comparison of Recurrent and Convolutional Deep Learning Architectures for EEG Seizure Forecasting

Sina Shafiezadeh, Marco Pozza, Alberto Testolin

Abstract: Many research efforts are being spent to discover predictive markers of seizures, which would allow to build forecasting systems that could mitigate the risk of injuries and clinical complications in epileptic patients. Although electroencephalography (EEG) is the most widely used tool to monitor abnormal brain electrical activity, no commercial devices can reliably anticipate seizures from EEG signal analysis at present. Recent advances in Artificial Intelligence, particularly deep learning algorithms, show promise in enhancing EEG classifier forecasting accuracy by automatically extracting relevant spatio-temporal features from EEG recordings. In this study, we systematically compare the predictive accuracy of two leading deep learning architectures: recurrent models based on Long Short-Term Memory networks (LSTMs) and Convolutional Neural Networks (CNNs). To this aim, we consider a data set of long-term, continuous multi-channel EEG recordings collected from 29 epileptic patients