Maximum approximate entropy and threshold: A new approach for regularity changes detection

Autores
Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Torres, Maria Eugenia
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system’s dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension m, threshold r and data length N) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that rmax (ApEn(m,rmax,N)=ApEnmax) can also be used as a feature to discern between dynamics. Moreover, the combined use of ApEnmax and rmax allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When ApEnmax is incapable of discerning between different dynamics because of the noise presence, our results suggest that rmax provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of ApEnmax and rmax can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use.
Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Non-linear dynamics
Approximate entropy
Chaotic time-series
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33590
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Maximum approximate entropy and threshold: A new approach for regularity changes detectionRestrepo Rinckoar, Juan FelipeSchlotthauer, GastonTorres, Maria EugeniaNon-linear dynamicsApproximate entropyChaotic time-serieshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system’s dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension m, threshold r and data length N) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that rmax (ApEn(m,rmax,N)=ApEnmax) can also be used as a feature to discern between dynamics. Moreover, the combined use of ApEnmax and rmax allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When ApEnmax is incapable of discerning between different dynamics because of the noise presence, our results suggest that rmax provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of ApEnmax and rmax can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use.Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33590Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Torres, Maria Eugenia; Maximum approximate entropy and threshold: A new approach for regularity changes detection; Elsevier; Physica A: Statistical Mechanics and its Applications; 409; 5-2014; 97-1090378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2014.04.041info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437114003598info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1405.7637info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:00:22Zoai:ri.conicet.gov.ar:11336/33590instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:00:22.696CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Maximum approximate entropy and threshold: A new approach for regularity changes detection
title Maximum approximate entropy and threshold: A new approach for regularity changes detection
spellingShingle Maximum approximate entropy and threshold: A new approach for regularity changes detection
Restrepo Rinckoar, Juan Felipe
Non-linear dynamics
Approximate entropy
Chaotic time-series
title_short Maximum approximate entropy and threshold: A new approach for regularity changes detection
title_full Maximum approximate entropy and threshold: A new approach for regularity changes detection
title_fullStr Maximum approximate entropy and threshold: A new approach for regularity changes detection
title_full_unstemmed Maximum approximate entropy and threshold: A new approach for regularity changes detection
title_sort Maximum approximate entropy and threshold: A new approach for regularity changes detection
dc.creator.none.fl_str_mv Restrepo Rinckoar, Juan Felipe
Schlotthauer, Gaston
Torres, Maria Eugenia
author Restrepo Rinckoar, Juan Felipe
author_facet Restrepo Rinckoar, Juan Felipe
Schlotthauer, Gaston
Torres, Maria Eugenia
author_role author
author2 Schlotthauer, Gaston
Torres, Maria Eugenia
author2_role author
author
dc.subject.none.fl_str_mv Non-linear dynamics
Approximate entropy
Chaotic time-series
topic Non-linear dynamics
Approximate entropy
Chaotic time-series
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system’s dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension m, threshold r and data length N) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that rmax (ApEn(m,rmax,N)=ApEnmax) can also be used as a feature to discern between dynamics. Moreover, the combined use of ApEnmax and rmax allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When ApEnmax is incapable of discerning between different dynamics because of the noise presence, our results suggest that rmax provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of ApEnmax and rmax can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use.
Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Torres, Maria Eugenia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system’s dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension m, threshold r and data length N) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that rmax (ApEn(m,rmax,N)=ApEnmax) can also be used as a feature to discern between dynamics. Moreover, the combined use of ApEnmax and rmax allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When ApEnmax is incapable of discerning between different dynamics because of the noise presence, our results suggest that rmax provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of ApEnmax and rmax can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/33590
Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Torres, Maria Eugenia; Maximum approximate entropy and threshold: A new approach for regularity changes detection; Elsevier; Physica A: Statistical Mechanics and its Applications; 409; 5-2014; 97-109
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33590
identifier_str_mv Restrepo Rinckoar, Juan Felipe; Schlotthauer, Gaston; Torres, Maria Eugenia; Maximum approximate entropy and threshold: A new approach for regularity changes detection; Elsevier; Physica A: Statistical Mechanics and its Applications; 409; 5-2014; 97-109
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2014.04.041
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437114003598
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1405.7637
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.48226

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