Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration

[EN] Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, spec...

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Autores: Cano-Serrano, Jesús, Bertomeu-González, Vicente, Fácila, Lorenzo, Hornenro, Fernando, Alcaraz, Raúl, Rieta, J J|||0000-0002-3364-6380
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/205631
Acesso em linha:https://riunet.upv.es/handle/10251/205631
Access Level:acceso abierto
Palavra-chave:Blood pressure
Hypertension
Photoplethysmography
Calibration
Deep learning
TECNOLOGIA ELECTRONICA
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oai_identifier_str oai:riunet.upv.es:10251/205631
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
title Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
spellingShingle Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
Cano-Serrano, Jesús
Blood pressure
Hypertension
Photoplethysmography
Calibration
Deep learning
TECNOLOGIA ELECTRONICA
title_short Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
title_full Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
title_fullStr Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
title_full_unstemmed Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
title_sort Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration
dc.creator.none.fl_str_mv Cano-Serrano, Jesús
Bertomeu-González, Vicente
Fácila, Lorenzo
Hornenro, Fernando
Alcaraz, Raúl
Rieta, J J|||0000-0002-3364-6380
author Cano-Serrano, Jesús
author_facet Cano-Serrano, Jesús
Bertomeu-González, Vicente
Fácila, Lorenzo
Hornenro, Fernando
Alcaraz, Raúl
Rieta, J J|||0000-0002-3364-6380
author_role author
author2 Bertomeu-González, Vicente
Fácila, Lorenzo
Hornenro, Fernando
Alcaraz, Raúl
Rieta, J J|||0000-0002-3364-6380
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Electrónica
Escuela Politécnica Superior de Gandia
Biosignals & Minimally Invasive Technologies-BioMIT
Generalitat Valenciana
Agencia Estatal de Investigación
Ministerio de Ciencia e Innovación
Junta de Comunidades de Castilla-La Mancha
Consejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La Mancha
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Blood pressure
Hypertension
Photoplethysmography
Calibration
Deep learning
TECNOLOGIA ELECTRONICA
topic Blood pressure
Hypertension
Photoplethysmography
Calibration
Deep learning
TECNOLOGIA ELECTRONICA
description [EN] Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-12-18
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/205631
url https://riunet.upv.es/handle/10251/205631
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-111100RB-C21 ALGORITMOS AGILES, INTERNET DE LAS COSAS, Y ANALITICA DE DATOS PARA UN TRANSPORTE SOSTENIBLE EN CIUDADES INTELIGENTES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123804OB-I00 INTELIGENCIA ARTIFICIAL PARA LA MEDICINA MOVIL INNOVADORA EN ENFERMEDADES CARDIOVASCULARES
Consejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La Mancha Consejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La Mancha SBPLY%2F17%2F180501%2F000411 Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular
Generalitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2021%2F286 Inteligencia Artificial para Revolucionar la Medicina Móvil Usando Dispositivos Llevables
Junta de Comunidades de Castilla-La Mancha https://doi.org/10.13039/501100011698 SBPLY%2F21%2F180501%2F000186
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128525OB-I00 DETECCION PRECOZ DE ARRITMIAS CARDIACAS MEDIANTE INTELIGENCIA ARTIFICIAL PARA MEJORAR LA PREVENCION SECUNDARIA DEL ICTUS CRIPTOGENICO
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TED2021-130935B-I00
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 PID2019-111100RB-C21 Efficient & Sustainable Transport Systems in Smart Cities, Internet of Things, Transport Analytics, and Agile Algorithms
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and CalibrationCano-Serrano, JesúsBertomeu-González, VicenteFácila, LorenzoHornenro, FernandoAlcaraz, RaúlRieta, J J|||0000-0002-3364-6380Blood pressureHypertensionPhotoplethysmographyCalibrationDeep learningTECNOLOGIA ELECTRONICA[EN] Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.This research has received financial support from public grants PID2021-123804OB-I00, PID2021-00X128525-IV0 and TED2021-130935B-I00 of the Spanish Government, jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana.MDPI AGDepartamento de Ingeniería ElectrónicaEscuela Politécnica Superior de GandiaBiosignals & Minimally Invasive Technologies-BioMITGeneralitat ValencianaAgencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónJunta de Comunidades de Castilla-La ManchaConsejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La ManchaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-12-18journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/205631reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-111100RB-C21 ALGORITMOS AGILES, INTERNET DE LAS COSAS, Y ANALITICA DE DATOS PARA UN TRANSPORTE SOSTENIBLE EN CIUDADES INTELIGENTESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123804OB-I00 INTELIGENCIA ARTIFICIAL PARA LA MEDICINA MOVIL INNOVADORA EN ENFERMEDADES CARDIOVASCULARESConsejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La Mancha Consejería de Educación, Cultura y Deportes de la Junta de Comunidades de Castilla-La Mancha SBPLY%2F17%2F180501%2F000411 Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricularGeneralitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2021%2F286 Inteligencia Artificial para Revolucionar la Medicina Móvil Usando Dispositivos LlevablesJunta de Comunidades de Castilla-La Mancha https://doi.org/10.13039/501100011698 SBPLY%2F21%2F180501%2F000186Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128525OB-I00 DETECCION PRECOZ DE ARRITMIAS CARDIACAS MEDIANTE INTELIGENCIA ARTIFICIAL PARA MEJORAR LA PREVENCION SECUNDARIA DEL ICTUS CRIPTOGENICOMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TED2021-130935B-I00Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 PID2019-111100RB-C21 Efficient & Sustainable Transport Systems in Smart Cities, Internet of Things, Transport Analytics, and Agile Algorithmsopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2056312026-06-13T07:49:27Z
score 15.198674

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