Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier

Autores
Redelico, Francisco Oscar; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo Aníbal; Risk, Marcelo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals.
Fil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: García, María del Carmen. Hospital Italiano; Argentina
Fil: Silva, Walter. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rosso, Osvaldo Aníbal. Hospital Italiano; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina. Hospital Italiano; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
CLASSIFICATION ANALYSIS
ELECTROENCEPHALOGRAPHY
PERMUTATION ENTROPY
PERMUTATION MIN-ENTROPY
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:11336/72557
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifierRedelico, Francisco OscarTraversaro Varela, FranciscoGarcía, María del CarmenSilva, WalterRosso, Osvaldo AníbalRisk, MarceloCLASSIFICATION ANALYSISELECTROENCEPHALOGRAPHYPERMUTATION ENTROPYPERMUTATION MIN-ENTROPYhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals.Fil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: García, María del Carmen. Hospital Italiano; ArgentinaFil: Silva, Walter. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rosso, Osvaldo Aníbal. Hospital Italiano; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina. Hospital Italiano; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMolecular Diversity Preservation International2017-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/72557Redelico, Francisco Oscar; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo Aníbal; et al.; Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier; Molecular Diversity Preservation International; Entropy; 19; 2; 2-2017; 1-121099-4300CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1099-4300/19/2/72info:eu-repo/semantics/altIdentifier/doi/10.3390/e19020072info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:27Zoai:ri.conicet.gov.ar:11336/72557instacron: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:34982025-09-03 09:57:27.775CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
title Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
spellingShingle Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
Redelico, Francisco Oscar
CLASSIFICATION ANALYSIS
ELECTROENCEPHALOGRAPHY
PERMUTATION ENTROPY
PERMUTATION MIN-ENTROPY
title_short Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
title_full Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
title_fullStr Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
title_full_unstemmed Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
title_sort Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
dc.creator.none.fl_str_mv Redelico, Francisco Oscar
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo Aníbal
Risk, Marcelo
author Redelico, Francisco Oscar
author_facet Redelico, Francisco Oscar
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo Aníbal
Risk, Marcelo
author_role author
author2 Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo Aníbal
Risk, Marcelo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CLASSIFICATION ANALYSIS
ELECTROENCEPHALOGRAPHY
PERMUTATION ENTROPY
PERMUTATION MIN-ENTROPY
topic CLASSIFICATION ANALYSIS
ELECTROENCEPHALOGRAPHY
PERMUTATION ENTROPY
PERMUTATION MIN-ENTROPY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals.
Fil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: García, María del Carmen. Hospital Italiano; Argentina
Fil: Silva, Walter. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rosso, Osvaldo Aníbal. Hospital Italiano; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina. Hospital Italiano; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals.
publishDate 2017
dc.date.none.fl_str_mv 2017-02
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/11336/72557
Redelico, Francisco Oscar; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo Aníbal; et al.; Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier; Molecular Diversity Preservation International; Entropy; 19; 2; 2-2017; 1-12
1099-4300
CONICET Digital
CONICET
url http://hdl.handle.net/11336/72557
identifier_str_mv Redelico, Francisco Oscar; Traversaro Varela, Francisco; García, María del Carmen; Silva, Walter; Rosso, Osvaldo Aníbal; et al.; Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier; Molecular Diversity Preservation International; Entropy; 19; 2; 2-2017; 1-12
1099-4300
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1099-4300/19/2/72
info:eu-repo/semantics/altIdentifier/doi/10.3390/e19020072
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
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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 13.13397

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