Semi-Supervised Deep Learning System for Epileptic Seizures Onset Prediction

Abstract

The advance prediction of seizures before its onset has been a challenging task for scientists for a long time. It is still the epileptic patients' hope to find an effective way of preventing seizures to improve the quality of their lives. In this paper, using an innovative mixing of unsupervised and supervised deep learning techniques, we propose a novel epileptic seizure prediction system using electroencephalogram (EEG) recordings from the human brains. The proposed system is built upon classifying between the interictal and the preictal brain states. The proposed system uses two-dimensional deep convolutional autoencoder for learning the best discriminative spatial features from the multichannel unlabeled raw EEG recordings. A Bidirectional Long Short-Term Memory recurrent neural network is used for classification based on the temporal information. To help achieve faster learning and reliable convergence for our system, the transfer learning technique is used for initializing the weights for the patient-specific networks. Within, up to one hour of prediction window, our system achieved an average sensitivity of 94.6% and average low false prediction alarm rate of 0.04FP/h which makes it one of the most efficient among state-of-the-art methods.