Seizure Prediction by Multivariate Autoregressive Model Order Optimization

Abstract

For several decades, researchers are aiming for the detection of precursors of epileptic seizures. A system that is able to issue a warning about an impending seizure could dramatically improve the quality of life of affected patients. In this work, we apply multivariate autoregressive (MVAR) modeling to intracranial electroencephalography (iEEG) recordings of patients with therapy resistant epilepsy. As compared to our previous investigations, we studied the optimal model order of the autoregressive process as a feature for seizure prediction. In a statistical evaluation, we obtain significant results for 17 out of 20 patients.

Keywords

• seizure prediction
• epilepsy
• multivariate autoregressive model
• model order
• electroencephalography