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Regularity and Matching Pursuit Feature Extraction for the Detection of Epileptic Seizures

hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorZ-FLORES, Emigdio
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorTRUJILLO, Leonardo
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorSOTELO, Arturo
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorCORIA, Luis
dc.date.accessioned2024-04-04T03:13:19Z
dc.date.available2024-04-04T03:13:19Z
dc.date.issued2016
dc.identifier.issn0165-0270
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193942
dc.description.abstractEnBackgroundThe 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.MethodsIn 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.ResultsSeveral 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.ConclusionsThe 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.
dc.language.isoen
dc.publisherElsevier
dc.title.enRegularity and Matching Pursuit Feature Extraction for the Detection of Epileptic Seizures
dc.typeArticle de revue
dc.identifier.doi10.1016/j.jneumeth.2016.03.024
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.description.sponsorshipEuropeAnalysis and classification of mental states of vigilance with evolutionary computation
bordeaux.journalJournal of Neuroscience Methods
bordeaux.page107–125
bordeaux.volume266
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01389051
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01389051v1
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