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

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...

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Bibliographic Details
Published in2017 IEEE URUCON pp. 1 - 4
Main Authors Quintero-Rincon, Antonio, Prendes, Jorge, Muro, Valeria, D'Giano, Carlos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Brain modeling
EEG
Electroencephalography
Epilepsy
k-nearest neighbors
Morphology
Probability density function
Shape
spike-and-wave
t-locationscale distribution
Training
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Summary: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.
DOI:10.1109/URUCON.2017.8171869