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Entropy 2017, 19(2), 72; doi:10.3390/e19020072

Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier

1
Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, C1199ABB Ciudad Autónoma de Buenos Aires, Argentina
2
Instituto Tecnológico de Buenos Aires (ITBA), C1106ACD Ciudad Autónoma de Buenos Aires, Argentina
3
Servicio de Neurología, Hospital Italiano de Buenos Aires, Gral. Juan Domingo Perón 4190, C1199ABB Ciudad Autónoma de Buenos Aires, Argentina
4
Servicio de Neurologia Infantil—Instituto Universitario (IUHI)—Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, C1199ABB Ciudad Autónoma de Buenos Aires, Argentina
5
Instituto de Física, Universidade Federal de Alagoas (UFAL), 57072-900 Maceió, Brazil
6
Complex Systems Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, 12455 Santiago, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 17 November 2016 / Revised: 26 January 2017 / Accepted: 10 February 2017 / Published: 16 February 2017
(This article belongs to the Special Issue Entropy and Electroencephalography II)
View Full-Text   |   Download PDF [475 KB, uploaded 17 February 2017]   |  

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. View Full-Text
Keywords: permutation entropy; permutation min-entropy; electroencephalography; classification analysis permutation entropy; permutation min-entropy; electroencephalography; classification analysis
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MDPI and ACS Style

Redelico, F.O.; Traversaro, F.; García, M.C.; Silva, W.; Rosso, O.A.; Risk, M. Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier. Entropy 2017, 19, 72.

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