Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights

Autores
Ipar, Eugenia; Cymberknop, Leandro Javier; Armentano, Ricardo Luis
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.
Fil: Ipar, Eugenia. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cymberknop, Leandro Javier. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina
Fil: Armentano, Ricardo Luis. Universidad de la República; Uruguay
Materia
vascular age
machine learning
arterial pressure waveform
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/242851
Acceder
id CONICETDig_ff441a8774af2c2601886d84b6c528d8
oai_identifier_str oai:ri.conicet.gov.ar:11336/242851
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor InsightsIpar, EugeniaCymberknop, Leandro JavierArmentano, Ricardo Luisvascular agemachine learningarterial pressure waveformhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.Fil: Ipar, Eugenia. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cymberknop, Leandro Javier. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; ArgentinaFil: Armentano, Ricardo Luis. Universidad de la República; UruguayMDPI2023-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/242851Ipar, Eugenia; Cymberknop, Leandro Javier; Armentano, Ricardo Luis; Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights; MDPI; Applied Sciences; 13; 19; 9-2023; 1-222076-3417CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/13/19/10585info:eu-repo/semantics/altIdentifier/doi/10.3390/app131910585info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:29Zoai:ri.conicet.gov.ar:11336/242851instacron: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:34982025-09-03 10:05:29.323CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
title Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
spellingShingle Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
Ipar, Eugenia
vascular age
machine learning
arterial pressure waveform
title_short Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
title_full Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
title_fullStr Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
title_full_unstemmed Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
title_sort Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
dc.creator.none.fl_str_mv Ipar, Eugenia
Cymberknop, Leandro Javier
Armentano, Ricardo Luis
author Ipar, Eugenia
author_facet Ipar, Eugenia
Cymberknop, Leandro Javier
Armentano, Ricardo Luis
author_role author
author2 Cymberknop, Leandro Javier
Armentano, Ricardo Luis
author2_role author
author
dc.subject.none.fl_str_mv vascular age
machine learning
arterial pressure waveform
topic vascular age
machine learning
arterial pressure waveform
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.
Fil: Ipar, Eugenia. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cymberknop, Leandro Javier. Universidad Tecnologica Nacional. Facultad Regional Buenos Aires. Grupo de Investigacion y Desarrollo En Bioingenieria.; Argentina
Fil: Armentano, Ricardo Luis. Universidad de la República; Uruguay
description With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
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/11336/242851
Ipar, Eugenia; Cymberknop, Leandro Javier; Armentano, Ricardo Luis; Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights; MDPI; Applied Sciences; 13; 19; 9-2023; 1-22
2076-3417
CONICET Digital
CONICET
url http://hdl.handle.net/11336/242851
identifier_str_mv Ipar, Eugenia; Cymberknop, Leandro Javier; Armentano, Ricardo Luis; Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights; MDPI; Applied Sciences; 13; 19; 9-2023; 1-22
2076-3417
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.mdpi.com/2076-3417/13/19/10585
info:eu-repo/semantics/altIdentifier/doi/10.3390/app131910585
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
_version_ 1842269912729387008
score 13.13397

Publicaciones similares