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

"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...

Descripción completa

Detalles Bibliográficos
Autores principales: Redelico, Francisco, Traversaro Varela, Francisco, García, María del Carmen, Silva, Walter, Rosso, Osvaldo A., Risk, Marcelo
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ELECTROENCEFALOGRAFIA
ENTROPIA
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1635
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1635
record_format dspace
spelling I32-R138-123456789-16352022-12-07T13:06:43Z Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier Redelico, Francisco Traversaro Varela, Francisco García, María del Carmen Silva, Walter Rosso, Osvaldo A. Risk, Marcelo ELECTROENCEFALOGRAFIA ENTROPIA "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." 2019-07-05T17:04:15Z 2019-07-05T17:04:15Z 2017-02 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion 1099-4300 http://ri.itba.edu.ar/handle/123456789/1635 en info:eu-repo/semantics/reference/doi/10.3390/e19020072 info:eu-repo/grantAgreement/CONICET/AR. Ciudad Autónoma de Buenos Aires http://creativecommons.org/licenses/by/4.0/ 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
ENTROPIA
spellingShingle ELECTROENCEFALOGRAFIA
ENTROPIA
Redelico, Francisco
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo A.
Risk, Marcelo
Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
topic_facet ELECTROENCEFALOGRAFIA
ENTROPIA
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."
format Artículos de Publicaciones Periódicas
publishedVersion
author Redelico, Francisco
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo A.
Risk, Marcelo
author_facet Redelico, Francisco
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo A.
Risk, Marcelo
author_sort Redelico, Francisco
title Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
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
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1635
work_keys_str_mv AT redelicofrancisco classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
AT traversarovarelafrancisco classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
AT garciamariadelcarmen classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
AT silvawalter classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
AT rossoosvaldoa classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
AT riskmarcelo classificationofnormalandpreictaleegsignalsusingpermutationentropiesandageneralizedlinearmodelasaclassifier
_version_ 1765661033305735168

Ejemplares similares