Dynamic Mode Decomposition (DMD) for Enhanced Epileptic Seizure Prediction from EEG Signals

Epilepsy is a non-communicable neurological disorder that causes recurrent and unprovoked seizure. Ideally, predicting seizures would represent a step forward in making life easier for those who suffer from epilepsy. This work aims to predict the occurence of epileptic seizures via a novel approach that combines Dynamic Mode Decomposition (DMD), that is a data-driven modelling technique for dynamical systems with a deep learning classifier, that is based on a convolutional neural network. The resulting two-stage data-driven predictor was tailored for the analysis of encephalographic (EEG) data. The validity analysis of the approach is carried out over the CHB-MIT Scalp EEG Database, demonstrating its applicability for seizures' recognition and prediction on real clinical data.