Improved complete ensemble EMD: A suitable tool for biomedical signal processing

Autores
Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons.
Fil: Colominas, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Materia
Empirical Mode Decomposition (Emd)
Noise-Assisted Data Analysis
Electroglottography
Ventricular Fibrillation
Epileptic Seizure
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/36429
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Improved complete ensemble EMD: A suitable tool for biomedical signal processingColominas, Marcelo AlejandroSchlotthauer, GastonTorres, Maria EugeniaEmpirical Mode Decomposition (Emd)Noise-Assisted Data AnalysisElectroglottographyVentricular FibrillationEpileptic Seizurehttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons.Fil: Colominas, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaElsevier2014-11info: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/11336/36429Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Improved complete ensemble EMD: A suitable tool for biomedical signal processing; Elsevier; Biomedical Signal Processing and Control; 14; 11-2014; 19-291746-8094CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.bspc.2014.06.009info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1746809414000962info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T11:47:13Zoai:ri.conicet.gov.ar:11336/36429instacron: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:34982025-10-29 11:47:13.673CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improved complete ensemble EMD: A suitable tool for biomedical signal processing
title Improved complete ensemble EMD: A suitable tool for biomedical signal processing
spellingShingle Improved complete ensemble EMD: A suitable tool for biomedical signal processing
Colominas, Marcelo Alejandro
Empirical Mode Decomposition (Emd)
Noise-Assisted Data Analysis
Electroglottography
Ventricular Fibrillation
Epileptic Seizure
title_short Improved complete ensemble EMD: A suitable tool for biomedical signal processing
title_full Improved complete ensemble EMD: A suitable tool for biomedical signal processing
title_fullStr Improved complete ensemble EMD: A suitable tool for biomedical signal processing
title_full_unstemmed Improved complete ensemble EMD: A suitable tool for biomedical signal processing
title_sort Improved complete ensemble EMD: A suitable tool for biomedical signal processing
dc.creator.none.fl_str_mv Colominas, Marcelo Alejandro
Schlotthauer, Gaston
Torres, Maria Eugenia
author Colominas, Marcelo Alejandro
author_facet Colominas, Marcelo Alejandro
Schlotthauer, Gaston
Torres, Maria Eugenia
author_role author
author2 Schlotthauer, Gaston
Torres, Maria Eugenia
author2_role author
author
dc.subject.none.fl_str_mv Empirical Mode Decomposition (Emd)
Noise-Assisted Data Analysis
Electroglottography
Ventricular Fibrillation
Epileptic Seizure
topic Empirical Mode Decomposition (Emd)
Noise-Assisted Data Analysis
Electroglottography
Ventricular Fibrillation
Epileptic Seizure
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons.
Fil: Colominas, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
description The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons.
publishDate 2014
dc.date.none.fl_str_mv 2014-11
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/11336/36429
Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Improved complete ensemble EMD: A suitable tool for biomedical signal processing; Elsevier; Biomedical Signal Processing and Control; 14; 11-2014; 19-29
1746-8094
CONICET Digital
CONICET
url http://hdl.handle.net/11336/36429
identifier_str_mv Colominas, Marcelo Alejandro; Schlotthauer, Gaston; Torres, Maria Eugenia; Improved complete ensemble EMD: A suitable tool for biomedical signal processing; Elsevier; Biomedical Signal Processing and Control; 14; 11-2014; 19-29
1746-8094
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.bspc.2014.06.009
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1746809414000962
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/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 13.10058

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