A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence

Autores
Quintero Rincón, Antonio; Pereyra, M.; D'Giano, Carlos; Batatia, H.; Risk, Marcelo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure.
Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Pereyra, M.. University Of Bristol; Reino Unido
Fil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Batatia, H.. Universite de Toulouse; Francia
Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentina
Materia
Epilepsy
Generalized Gaussian Distribution
Kullback-Leibler Divergence
Multivariate Wavelet Decomposition
Seizure/Non-Seizure
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/77054
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/77054
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergenceQuintero Rincón, AntonioPereyra, M.D'Giano, CarlosBatatia, H.Risk, MarceloEpilepsyGeneralized Gaussian DistributionKullback-Leibler DivergenceMultivariate Wavelet DecompositionSeizure/Non-Seizurehttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure.Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Pereyra, M.. University Of Bristol; Reino UnidoFil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Batatia, H.. Universite de Toulouse; FranciaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; ArgentinaSpringer Verlag2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/77054Quintero Rincón, Antonio; Pereyra, M.; D'Giano, Carlos; Batatia, H.; Risk, Marcelo; A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence; Springer Verlag; Ifmbe Proceedings; 60; 4-2017; 13-161680-0737CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-981-10-4086-3_4info:eu-repo/semantics/altIdentifier/doi/10.1007/978-981-10-4086-3_4info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:04:05Zoai:ri.conicet.gov.ar:11336/77054instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:04:05.997CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
spellingShingle A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
Quintero Rincón, Antonio
Epilepsy
Generalized Gaussian Distribution
Kullback-Leibler Divergence
Multivariate Wavelet Decomposition
Seizure/Non-Seizure
title_short A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_full A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_fullStr A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_full_unstemmed A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_sort A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
dc.creator.none.fl_str_mv Quintero Rincón, Antonio
Pereyra, M.
D'Giano, Carlos
Batatia, H.
Risk, Marcelo
author Quintero Rincón, Antonio
author_facet Quintero Rincón, Antonio
Pereyra, M.
D'Giano, Carlos
Batatia, H.
Risk, Marcelo
author_role author
author2 Pereyra, M.
D'Giano, Carlos
Batatia, H.
Risk, Marcelo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Epilepsy
Generalized Gaussian Distribution
Kullback-Leibler Divergence
Multivariate Wavelet Decomposition
Seizure/Non-Seizure
topic Epilepsy
Generalized Gaussian Distribution
Kullback-Leibler Divergence
Multivariate Wavelet Decomposition
Seizure/Non-Seizure
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure.
Fil: Quintero Rincón, Antonio. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Pereyra, M.. University Of Bristol; Reino Unido
Fil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Batatia, H.. Universite de Toulouse; Francia
Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentina
description This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/77054
Quintero Rincón, Antonio; Pereyra, M.; D'Giano, Carlos; Batatia, H.; Risk, Marcelo; A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence; Springer Verlag; Ifmbe Proceedings; 60; 4-2017; 13-16
1680-0737
CONICET Digital
CONICET
url http://hdl.handle.net/11336/77054
identifier_str_mv Quintero Rincón, Antonio; Pereyra, M.; D'Giano, Carlos; Batatia, H.; Risk, Marcelo; A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence; Springer Verlag; Ifmbe Proceedings; 60; 4-2017; 13-16
1680-0737
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-981-10-4086-3_4
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-981-10-4086-3_4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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