Epileptic seizure prediction using scalp electroencephalogram signals

Abstract

Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.