Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling Predicción de crisis epilépticas utilizando el coeficiente de correlación producto-momento de Pearson a partir de un clasificador lineal de la distribución Gaussiana generalizada

QUINTERO-RINCÓN, ANTONIO; D'GIANO, CARLOS; RISK, MARCELO

Neurologia Argentina

Ediciones Doyma, S.L.

Año: 2018 vol. 10 p. 210 - 217

1853-0028

To predict an epileptic event, means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications, is a typical problem in biomedical signal processing that help to an appropriate diagnosis and treatment of this disease. In this work we use Pearson´s product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing a good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearson´s test suggest that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearson product-moment correlation coefficient for the detection of epileptic seizures.