Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy

"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 cha...

Descripción completa

Detalles Bibliográficos
Autores principales: Traversaro Varela, Francisco, Redelico, Francisco
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
EPILEPSIA
ENTROPIA
ELECTROENCEFALOGRAFIA
ANALISIS DE SERIES DE TIEMPO
PROCESAMIENTO DE SEÑALES DIGITALES
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/2218
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-2218
record_format dspace
spelling I32-R138-123456789-22182022-12-07T13:06:42Z Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy Traversaro Varela, Francisco Redelico, Francisco EPILEPSIA ENTROPIA ELECTROENCEFALOGRAFIA ANALISIS DE SERIES DE TIEMPO PROCESAMIENTO DE SEÑALES DIGITALES "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." 2020-06-24T16:38:19Z 2020-06-24T16:38:19Z 2018-04 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 1007-5704 http://ri.itba.edu.ar/handle/123456789/2218 en info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cnsns.2017.10.013 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic EPILEPSIA
ENTROPIA
ELECTROENCEFALOGRAFIA
ANALISIS DE SERIES DE TIEMPO
PROCESAMIENTO DE SEÑALES DIGITALES
spellingShingle EPILEPSIA
ENTROPIA
ELECTROENCEFALOGRAFIA
ANALISIS DE SERIES DE TIEMPO
PROCESAMIENTO DE SEÑALES DIGITALES
Traversaro Varela, Francisco
Redelico, Francisco
Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
topic_facet EPILEPSIA
ENTROPIA
ELECTROENCEFALOGRAFIA
ANALISIS DE SERIES DE TIEMPO
PROCESAMIENTO DE SEÑALES DIGITALES
description "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."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Traversaro Varela, Francisco
Redelico, Francisco
author_facet Traversaro Varela, Francisco
Redelico, Francisco
author_sort Traversaro Varela, Francisco
title Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
title_short Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
title_full Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
title_fullStr Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
title_full_unstemmed Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy
title_sort confidence intervals and hypothesis testing for the permutation entropy with an application to epilepsy
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/2218
work_keys_str_mv AT traversarovarelafrancisco confidenceintervalsandhypothesistestingforthepermutationentropywithanapplicationtoepilepsy
AT redelicofrancisco confidenceintervalsandhypothesistestingforthepermutationentropywithanapplicationtoepilepsy
_version_ 1765661041111334912

Ejemplares similares