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

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. Re- cent 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 using a standard set of 20 channels. Our results demonstrate the superior performance of deep learning algorithms, which can achieve up to 99% accuracy, sensitivity, and specificity compared to more traditional machine learning approaches, which settle around 75% in all evalu- ation metrics. Our results also show that giving as input the recordings from all electrodes allows to exploit useful channel correlations to learn more robust predictive features, compared to convolutional models that treat each channel independently. We conclude that deep learning architectures hold promise for enhancing the diagnosis and prediction of epileptic seizures, offering potential benefits to those affected by such invali- dating neurological conditions.