Emigdio Z-Flores1, Leonardo Trujillo2, Arturo Sotelo3, Pierrick Legrand4, Luis N Coria5. 1. Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., Mexico. Electronic address: emigdioz@gmail.com. 2. Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., Mexico. Electronic address: leonardo.trujillo@tectijuana.edu.mx. 3. Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., Mexico. Electronic address: asotelo@tectijuana.mx. 4. Université of Bordeaux, UF Mathématiques et Interactions, 3ter place de la Victoire, 33076 Bordeaux, France; Inria Bordeaux Sud-Ouest, CQFD Team, 200 Avenue de la Vieille Tour, 33405 Talence, France; IMB UMR CNRS 5251, 351 Cours de la Libération, 33405 Talence, France. Electronic address: pierrick.legrand@u-bordeaux.fr. 5. Posgrado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., Mexico. Electronic address: luis.coria@gmail.com.
Abstract
BACKGROUND: 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. METHODS: 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 the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. RESULTS: Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. COMPARISON WITH EXISTING METHODS: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. CONCLUSIONS: The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.
BACKGROUND: 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. METHODS: 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 the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. RESULTS: Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. COMPARISON WITH EXISTING METHODS: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. CONCLUSIONS: The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.