artículo de publicación periódica.page.titleprefix
Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier

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dc.contributor.authorRedelico, Francisco
dc.contributor.authorTraversaro Varela, Francisco
dc.contributor.authorGarcía, María del Carmen
dc.contributor.authorSilva, Walter
dc.contributor.authorRosso, Osvaldo A.
dc.contributor.authorRisk, Marcelo
dc.date.accessioned2019-07-05T17:04:15Z
dc.date.available2019-07-05T17:04:15Z
dc.date.issued2017-02
dc.description.abstract"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."en
dc.identifier.issn1099-4300
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1635
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/reference/doi/10.3390/e19020072
dc.relationinfo:eu-repo/grantAgreement/CONICET/AR. Ciudad Autónoma de Buenos Aires
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.subjectELECTROENCEFALOGRAFIAes
dc.subjectENTROPIAes
dc.titleClassification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifieren
dc.typeArtículos de Publicaciones Periódicases
dc.typeinfo:eu-repo/semantics/publishedVersion
dspace.entity.typeArtículo de Publicación Periódica
itba.description.filiationFil: Redelico, Francisco. Hospital Italiano de Buenos Aires; Argentina.
itba.description.filiationFil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: García, María del Carmen. Hospital Italiano de Buenos Aires; Argentina.
itba.description.filiationFil: Silva, Walter. Hospital Italiano de Buenos Aires; Argentina.
itba.description.filiationFil: Rosso, Osvaldo A. Hospital Italiano de Buenos Aires; Argentina.
itba.description.filiationFil: Rosso, Osvaldo A. Universidade Federal de Alagoas; Brasil.
itba.description.filiationFil: Rosso, Osvaldo A. Universidad de los Andes; Chile.
itba.description.filiationFil: Risk, Marcelo. Hospital Italiano de Buenos Aires; Argentina.
itba.description.filiationFil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina.

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