Loading...
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
Redelico, Francisco
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo A.
Risk, Marcelo
Type
Artículo de publicación periódica
Citations
Altmetric:
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."
Description
Date
2017-02
Journal Title
Journal ISSN
Volume Title
Publisher
Collections
Files
Loading...
Artículo_Redelico
Adobe PDF, 473.58 KB
Research Projects
Organizational Units
Journal Issue
Keywords
ELECTROENCEFALOGRAFIA, ENTROPIA