Measuring complexity of biomedical signals

Autores
Schlotthauer, Gaston; Humeau-Heurtier, Anne; Escudero, Javier; Rufiner, Hugo Leonardo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is well known that biomedical signals, such as heart rate variability (HRV), electrocardiogram (ECG), electroencephalogram (EEG), and voice, arise from complex nonlinear dynamical systems, as the cardiovascular, nervous, or phonatory ones. Information extracted from these signals provides insights regarding the status of the underlying physiology. Complexity measures are helpful to quantitatively describe nonlinear biomedical systems and to detect changes in their dynamics that can be associated with physiological or pathological events. These measures on biomedical signals and images can be used in a wide field of applications as pathology detection, decision support systems, treatment monitoring, and temporal segmentation. They can also be used to characterize biomedical systems that gave rise to those images and time series. However, in practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameter tuning. Many of these issues are still unsolved. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue. It is focused not only on the application of existing complexity measures on biomedical signals and images but also on the development of new complexity measure algorithms. Some interesting complexity-based works are also associated with machine learning-based strategies, automatization in parameter setting, and applications in pattern recognition problems, as well as developments and applications of novel complexity estimators for multivariate, multiscale, or multimodal data. In this context, different proposals that explore theory and applications of complexity-based measures related to biomedical signal problems were selected. After a rigorous review process, 8 papers have been accepted for this special issue.
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; Argentina
Fil: Humeau-Heurtier, Anne. Université D'angers; Francia
Fil: Escudero, Javier. University of Edinburgh; Reino Unido
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral; Argentina
Materia
COMPLEXITY MEASURES
BIOMEDICAL SIGNALS
DIGITAL SIGNAL PROCESSING
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:11336/87142

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spelling Measuring complexity of biomedical signalsSchlotthauer, GastonHumeau-Heurtier, AnneEscudero, JavierRufiner, Hugo LeonardoCOMPLEXITY MEASURESBIOMEDICAL SIGNALSDIGITAL SIGNAL PROCESSINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1It is well known that biomedical signals, such as heart rate variability (HRV), electrocardiogram (ECG), electroencephalogram (EEG), and voice, arise from complex nonlinear dynamical systems, as the cardiovascular, nervous, or phonatory ones. Information extracted from these signals provides insights regarding the status of the underlying physiology. Complexity measures are helpful to quantitatively describe nonlinear biomedical systems and to detect changes in their dynamics that can be associated with physiological or pathological events. These measures on biomedical signals and images can be used in a wide field of applications as pathology detection, decision support systems, treatment monitoring, and temporal segmentation. They can also be used to characterize biomedical systems that gave rise to those images and time series. However, in practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameter tuning. Many of these issues are still unsolved. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue. It is focused not only on the application of existing complexity measures on biomedical signals and images but also on the development of new complexity measure algorithms. Some interesting complexity-based works are also associated with machine learning-based strategies, automatization in parameter setting, and applications in pattern recognition problems, as well as developments and applications of novel complexity estimators for multivariate, multiscale, or multimodal data. In this context, different proposals that explore theory and applications of complexity-based measures related to biomedical signal problems were selected. After a rigorous review process, 8 papers have been accepted for this special issue.Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; ArgentinaFil: Humeau-Heurtier, Anne. Université D'angers; FranciaFil: Escudero, Javier. University of Edinburgh; Reino UnidoFil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral; ArgentinaJohn Wiley & Sons Inc2018-09info: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/11336/87142Schlotthauer, Gaston; Humeau-Heurtier, Anne; Escudero, Javier; Rufiner, Hugo Leonardo; Measuring complexity of biomedical signals; John Wiley & Sons Inc; Complexity; 2018; 9-2018; 1-31076-2787CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2018/5408254/info:eu-repo/semantics/altIdentifier/doi/10.1155/2018/5408254info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:07:05Zoai:ri.conicet.gov.ar:11336/87142instacron: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:34982025-09-29 10:07:05.492CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Measuring complexity of biomedical signals
title Measuring complexity of biomedical signals
spellingShingle Measuring complexity of biomedical signals
Schlotthauer, Gaston
COMPLEXITY MEASURES
BIOMEDICAL SIGNALS
DIGITAL SIGNAL PROCESSING
title_short Measuring complexity of biomedical signals
title_full Measuring complexity of biomedical signals
title_fullStr Measuring complexity of biomedical signals
title_full_unstemmed Measuring complexity of biomedical signals
title_sort Measuring complexity of biomedical signals
dc.creator.none.fl_str_mv Schlotthauer, Gaston
Humeau-Heurtier, Anne
Escudero, Javier
Rufiner, Hugo Leonardo
author Schlotthauer, Gaston
author_facet Schlotthauer, Gaston
Humeau-Heurtier, Anne
Escudero, Javier
Rufiner, Hugo Leonardo
author_role author
author2 Humeau-Heurtier, Anne
Escudero, Javier
Rufiner, Hugo Leonardo
author2_role author
author
author
dc.subject.none.fl_str_mv COMPLEXITY MEASURES
BIOMEDICAL SIGNALS
DIGITAL SIGNAL PROCESSING
topic COMPLEXITY MEASURES
BIOMEDICAL SIGNALS
DIGITAL SIGNAL PROCESSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv It is well known that biomedical signals, such as heart rate variability (HRV), electrocardiogram (ECG), electroencephalogram (EEG), and voice, arise from complex nonlinear dynamical systems, as the cardiovascular, nervous, or phonatory ones. Information extracted from these signals provides insights regarding the status of the underlying physiology. Complexity measures are helpful to quantitatively describe nonlinear biomedical systems and to detect changes in their dynamics that can be associated with physiological or pathological events. These measures on biomedical signals and images can be used in a wide field of applications as pathology detection, decision support systems, treatment monitoring, and temporal segmentation. They can also be used to characterize biomedical systems that gave rise to those images and time series. However, in practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameter tuning. Many of these issues are still unsolved. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue. It is focused not only on the application of existing complexity measures on biomedical signals and images but also on the development of new complexity measure algorithms. Some interesting complexity-based works are also associated with machine learning-based strategies, automatization in parameter setting, and applications in pattern recognition problems, as well as developments and applications of novel complexity estimators for multivariate, multiscale, or multimodal data. In this context, different proposals that explore theory and applications of complexity-based measures related to biomedical signal problems were selected. After a rigorous review process, 8 papers have been accepted for this special issue.
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; Argentina
Fil: Humeau-Heurtier, Anne. Université D'angers; Francia
Fil: Escudero, Javier. University of Edinburgh; Reino Unido
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral; Argentina
description It is well known that biomedical signals, such as heart rate variability (HRV), electrocardiogram (ECG), electroencephalogram (EEG), and voice, arise from complex nonlinear dynamical systems, as the cardiovascular, nervous, or phonatory ones. Information extracted from these signals provides insights regarding the status of the underlying physiology. Complexity measures are helpful to quantitatively describe nonlinear biomedical systems and to detect changes in their dynamics that can be associated with physiological or pathological events. These measures on biomedical signals and images can be used in a wide field of applications as pathology detection, decision support systems, treatment monitoring, and temporal segmentation. They can also be used to characterize biomedical systems that gave rise to those images and time series. However, in practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameter tuning. Many of these issues are still unsolved. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue. It is focused not only on the application of existing complexity measures on biomedical signals and images but also on the development of new complexity measure algorithms. Some interesting complexity-based works are also associated with machine learning-based strategies, automatization in parameter setting, and applications in pattern recognition problems, as well as developments and applications of novel complexity estimators for multivariate, multiscale, or multimodal data. In this context, different proposals that explore theory and applications of complexity-based measures related to biomedical signal problems were selected. After a rigorous review process, 8 papers have been accepted for this special issue.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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/11336/87142
Schlotthauer, Gaston; Humeau-Heurtier, Anne; Escudero, Javier; Rufiner, Hugo Leonardo; Measuring complexity of biomedical signals; John Wiley & Sons Inc; Complexity; 2018; 9-2018; 1-3
1076-2787
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87142
identifier_str_mv Schlotthauer, Gaston; Humeau-Heurtier, Anne; Escudero, Javier; Rufiner, Hugo Leonardo; Measuring complexity of biomedical signals; John Wiley & Sons Inc; Complexity; 2018; 9-2018; 1-3
1076-2787
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2018/5408254/
info:eu-repo/semantics/altIdentifier/doi/10.1155/2018/5408254
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 John Wiley & Sons Inc
publisher.none.fl_str_mv John Wiley & Sons Inc
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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