Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the k-nearest neighbors classifier

Antonio Quintero Rincon (1) , Jorge Prendes (2) , Valeria Muro (3) , Carlos d'Giano (3)

1 ITBA - Instituto Tecnológico de Buenos Aires
2 IRIT-SC - Signal et Communications
3 Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia - FLENI (ARGENTINA)

Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In this paper we propose a k-nearest neighbors classification for epileptic EEG signals based on an t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity and specificity.

Mots clés: Spike-and-wave; Epilepsy; T-locationscale distribution; K-nearest neighbors; EEG

Domaines: Traitement du signal et de l'image [eess.SP]; Biotechnologies

Antonio Quintero Rincon, Jorge Prendes, Valeria Muro, Carlos d'Giano. Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the k-nearest neighbors classifier. URUCON, 2017 IEEE, Oct 2017, Montevideo, Uruguay. pp.125-128. ⟨hal-02134635⟩