artículo de publicación periódica.page.titleprefix Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
Loading...
Date
2018-04
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
"In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of
complexity is the Permutation Entropy. But there is still no known method to determine
the accuracy of this measure. There has been little research on the statistical properties of
this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/f
α noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals."
Description
Keywords
EPILEPSIA, ENTROPIA, ELECTROENCEFALOGRAFIA, ANALISIS DE SERIES DE TIEMPO, PROCESAMIENTO DE SEÑALES DIGITALES