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

Miniatura
Fecha
2017-12
Autores
Quintero-Rincón, Antonio
Prendes, Jorge
D'Giano, Carlos
Muro, Valeria
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
"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."
Descripción
Citación
Colecciones