Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on
autoregressive
modeling of the EEG time series and combines a
least-squares parameter estimator
for EEG feature extraction along with a
support vector machine
(SVM) for binary classification between
preictal/ictal
and
interictal
states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.