Robust feature learning method for epileptic seizures prediction based on long-term EEG signals

Deep learning (DL) has been expensively applied in multiple fields like computer vision, speech recognition and natural language processing. The field of Epileptic seizure prediction didn't receive the deserved attention by DL community, even though, deep neural networks can handle the challenging task of onsets prediction whilst achieving the highest rates of sensitivity, despite the complex nature of EEG signals. In the literature, this issue was addressed differently most of the time using handcrafted temporal and spectral features, machine learning techniques and rarely deep learning with extracted features. In this paper, we introduce an LSTM model designed to address the chaotic nature of an EEG signal in order to predict pre-ictal and inter-ictal states. Our model is evaluated on the publicly available CHBMIT database. We achieved an average sensitivity rate of 0.84 using a Raw EEG data segment as input to the LSTM model.