The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive...

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Detalles Bibliográficos
Autores: Cano, J, Facila, L, Gracia-Baena, JM, Zangroniz, R, Alcaraz, R, Rieta, JJ
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:INCLIVA
Repositorio:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
OAI Identifier:oai:incliva.fundanetsuite.com:p16714
Acceso en línea:https://incliva.portalinvestigacion.com/publicaciones/16714
Access Level:acceso abierto
Palabra clave:high blood pressure
hypertension
photoplethysmography
electrocardiography
calibration
classification models
machine learning
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spelling The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and ElectrocardiographyCano, JFacila, LGracia-Baena, JMZangroniz, RAlcaraz, RRieta, JJhigh blood pressurehypertensionphotoplethysmographyelectrocardiographycalibrationclassification modelsmachine learningThe detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.MDPI2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://incliva.portalinvestigacion.com/publicaciones/16714Biosensors-BaselISSN: 0265928XISSNe: 20796374reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVAinstname:INCLIVAInglésinfo:eu-repo/semantics/openAccessoai:incliva.fundanetsuite.com:p167142026-06-07T16:35:31Z
dc.title.none.fl_str_mv The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
title The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
spellingShingle The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
Cano, J
high blood pressure
hypertension
photoplethysmography
electrocardiography
calibration
classification models
machine learning
title_short The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
title_full The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
title_fullStr The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
title_full_unstemmed The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
title_sort The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography
dc.creator.none.fl_str_mv Cano, J
Facila, L
Gracia-Baena, JM
Zangroniz, R
Alcaraz, R
Rieta, JJ
author Cano, J
author_facet Cano, J
Facila, L
Gracia-Baena, JM
Zangroniz, R
Alcaraz, R
Rieta, JJ
author_role author
author2 Facila, L
Gracia-Baena, JM
Zangroniz, R
Alcaraz, R
Rieta, JJ
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv high blood pressure
hypertension
photoplethysmography
electrocardiography
calibration
classification models
machine learning
topic high blood pressure
hypertension
photoplethysmography
electrocardiography
calibration
classification models
machine learning
description The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://incliva.portalinvestigacion.com/publicaciones/16714
url https://incliva.portalinvestigacion.com/publicaciones/16714
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Biosensors-Basel
ISSN: 0265928X
ISSNe: 20796374
reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
instname:INCLIVA
instname_str INCLIVA
reponame_str r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
collection r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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