Epileptic seizure prediction with recurrent convolutional neural networks

Abstract

In this paper, the use of recurrent convolutional neural networks for predicting epileptic seizures is proposed. Effective methods for predicting epileptic seizures need to be developed for the design of diagnostic and therapeutic techniques that will prevent or mitigate epileptic seizures. Studies show that epileptic seizures appear as a consequence of temporal and spatially developed processes in epileptic networks. In many studies using different linear and nonlinear methods of measurement, the result is that the measurements are differentiated before the epileptic seizure takes place. In this study, the features extracted by different methods from the multi-channel EEG signals are transformed into multi-spectral image series by projecting depending on the placement of the electrodes. Recurrent convolutional neural networks are trained with the obtained multi-spectral image sequences to reveal spatial and temporal correlations in multi-channel EEG signals before the epileptic seizure.