Epileptic electroencephalography classification using Embedded Dynamic Mode Decomposition.

Objective: Seizure prediction devices for drug-resistant epileptic patients could lead to improved quality of life and new treatment options, but current approaches to classification of electroencephalography (EEG) segments for early identification of the pre-seizure state typically require many features and complex classifiers. We therefore propose a novel spatio-temporal EEG feature set that significantly aids in separation and easy classification of the interictal and preictal states.

Approach: We derive key spectral features from the Embedded Dynamic Mode Decomposition (EmDMD) of the brain state system.