Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks

Abstract

Automated epileptic seizure detection has been an active area of research for the last two decades. Yet few, if any, of these methods are used in clinical practice because they fail to generalize across different patient populations. We present three simple Convolutional Neural Network (CNN) architectures for seizure detection that are capable of generalizing across sites. The convolutional layers automatically learn robust and discriminative correlations directly from both the raw multichannel scalp electroencephalography (EEG) signal and its short-time spectral representation. The models are trained on the publicly available Children's Hospital of Boston (CHB) data set in a leave-one-patient-out cross validation strategy. The trained model is then tested on a data set recorded at the University of Wisconsin (UW). We demonstrate that our CNNs achieve higher sensitivity than competing baselines, with only a minor increase in false positive rate. To our knowledge, this is the first work to achieve inter-hospital seizure detection without a significant drop in performance, thus providing an important benchmark for the seizure detection field.