Detection of absence epileptic seizures using support vector machine

C. F. Reyes; T. J. Contreras; B. Tovar; L. I. Garay; M. A. Silva

doi:10.1109/ICEEE.2013.6676057

Detection of absence epileptic seizures using support vector machine

C. F. Reyes, T. J. Contreras, B. Tovar, L. I. Garay, M. A. Silva

Unidad Profesional Interdisciplinaria de Ingeniería y Tecnologías Avanzadas (UPIITA)

Producción científica: Contribución a una conferencia › Artículo › revisión exhaustiva

Resumen

An application of support vector machine is presented as a tool for events detection in the electroencephalogram recorded from a patient clinically diagnosed with absence epilepsy. A comparison of five kernels is shown (linear, quadratic, polynomial, RBP and MLP) evaluating their efficiency for the detection of this epileptic event occurrence. The kernel with the best performance is the quadratic, with 99.43% accuracy in this specific case.

Idioma original	Inglés
Páginas	132-137
Número de páginas	6
DOI	https://doi.org/10.1109/ICEEE.2013.6676057
Estado	Publicada - 2013
Evento	2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013 - Mexico City, México Duración: 30 sep. 2013 → 4 oct. 2013

Conferencia

Conferencia	2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013
País/Territorio	México
Ciudad	Mexico City
Período	30/09/13 → 4/10/13

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10.1109/ICEEE.2013.6676057

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title = "Detection of absence epileptic seizures using support vector machine",

abstract = "An application of support vector machine is presented as a tool for events detection in the electroencephalogram recorded from a patient clinically diagnosed with absence epilepsy. A comparison of five kernels is shown (linear, quadratic, polynomial, RBP and MLP) evaluating their efficiency for the detection of this epileptic event occurrence. The kernel with the best performance is the quadratic, with 99.43% accuracy in this specific case.",

keywords = "EEG, SVM, epilepsy, kernels",

author = "Reyes, {C. F.} and Contreras, {T. J.} and B. Tovar and Garay, {L. I.} and Silva, {M. A.}",

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doi = "10.1109/ICEEE.2013.6676057",

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Detection of absence epileptic seizures using support vector machine. / Reyes, C. F.; Contreras, T. J.; Tovar, B. et al.
2013. 132-137 Papel presentado en 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013, Mexico City, México.

Producción científica: Contribución a una conferencia › Artículo › revisión exhaustiva

TY - CONF

T1 - Detection of absence epileptic seizures using support vector machine

AU - Reyes, C. F.

AU - Contreras, T. J.

AU - Tovar, B.

AU - Garay, L. I.

AU - Silva, M. A.

PY - 2013

Y1 - 2013

N2 - An application of support vector machine is presented as a tool for events detection in the electroencephalogram recorded from a patient clinically diagnosed with absence epilepsy. A comparison of five kernels is shown (linear, quadratic, polynomial, RBP and MLP) evaluating their efficiency for the detection of this epileptic event occurrence. The kernel with the best performance is the quadratic, with 99.43% accuracy in this specific case.

AB - An application of support vector machine is presented as a tool for events detection in the electroencephalogram recorded from a patient clinically diagnosed with absence epilepsy. A comparison of five kernels is shown (linear, quadratic, polynomial, RBP and MLP) evaluating their efficiency for the detection of this epileptic event occurrence. The kernel with the best performance is the quadratic, with 99.43% accuracy in this specific case.

KW - EEG

KW - SVM

KW - epilepsy

KW - kernels

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U2 - 10.1109/ICEEE.2013.6676057

DO - 10.1109/ICEEE.2013.6676057

M3 - Artículo

SP - 132

EP - 137

T2 - 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013

Y2 - 30 September 2013 through 4 October 2013

ER -

Detection of absence epileptic seizures using support vector machine

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