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Section: New Results

Classification of Epileptic states

The following result has been obtained by Emigdio Z. FLores, Leonardo Trujillo and Pierrick Legrand (CQFD member).

The neurological disorder known as Epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and Matching Pursuit are used as the main feature extraction techniques, and are combined with basic statistics to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm. Furthermore, several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 92$\%$ accuracy on the most difficult case. A comparison with recent results in relevant literature using a well known database reveals that our proposal achieves state-of-the-art performance.

Keywords: Epilepsy detection, Hölderian regularity, Matching Pursuit, EEG Classification