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 epilepti...

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Detalles Bibliográficos
Autores principales: Quintero-Rincón, Antonio, Prendes, Jorge, D'Giano, Carlos, Muro, Valeria
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ELECTROENCEFALOGRAFIA
EPILEPSIA
COMPENSACION DEL MOVIMIENTO
PROCESAMIENTO DE SEÑALES DIGITALES
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1718
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1718
record_format dspace
spelling I32-R138-123456789-17182022-12-07T14:13:54Z Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier Quintero-Rincón, Antonio Prendes, Jorge D'Giano, Carlos Muro, Valeria ELECTROENCEFALOGRAFIA EPILEPSIA COMPENSACION DEL MOVIMIENTO PROCESAMIENTO DE SEÑALES DIGITALES "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." 2019-08-16T16:15:27Z 2019-08-16T16:15:27Z 2017-12 Ponencias en Congresos info:eu-repo/semantics/acceptedVersion 978-1538-63-397-7 http://ri.itba.edu.ar/handle/123456789/1718 en info:eu-repo/semantics/altIdentifier/doi/10.1109/URUCON.2017.8171869 info:eu-repo/grantAgreement/ITBA/ITBACyT/41/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic ELECTROENCEFALOGRAFIA
EPILEPSIA
COMPENSACION DEL MOVIMIENTO
PROCESAMIENTO DE SEÑALES DIGITALES
spellingShingle ELECTROENCEFALOGRAFIA
EPILEPSIA
COMPENSACION DEL MOVIMIENTO
PROCESAMIENTO DE SEÑALES DIGITALES
Quintero-Rincón, Antonio
Prendes, Jorge
D'Giano, Carlos
Muro, Valeria
Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
topic_facet ELECTROENCEFALOGRAFIA
EPILEPSIA
COMPENSACION DEL MOVIMIENTO
PROCESAMIENTO DE SEÑALES DIGITALES
description "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."
format Ponencias en Congresos
acceptedVersion
author Quintero-Rincón, Antonio
Prendes, Jorge
D'Giano, Carlos
Muro, Valeria
author_facet Quintero-Rincón, Antonio
Prendes, Jorge
D'Giano, Carlos
Muro, Valeria
author_sort Quintero-Rincón, Antonio
title Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
title_short Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
title_full Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
title_fullStr Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
title_full_unstemmed Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
title_sort study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the k-nearest neighbors classifier
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1718
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AT prendesjorge studyonspikeandwavedetectioninepilepticsignalsusingtlocationscaledistributionandtheknearestneighborsclassifier
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AT murovaleria studyonspikeandwavedetectioninepilepticsignalsusingtlocationscaledistributionandtheknearestneighborsclassifier
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