info:eu-repo/semantics/article
Classification of normal and pre-ictal EEG signals using permutation entropies and a generalized linear model as a classifier
Fecha
2017-02Registro en:
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
Autor
Redelico, Francisco Oscar
Traversaro Varela, Francisco
García, María del Carmen
Silva, Walter
Rosso, Osvaldo Aníbal
Risk, Marcelo
Resumen
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.