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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Autores: Cano Serrano, Jesús, Fácila Rubio, Lorenzo, Gracía Baena, Juan Manuel, Zangroniz Cantabrana, Roberto, Alcaraz Martínez, Raúl, Rieta Ibañez, José Joaquín
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/35130
Acceso en línea:https://doi.org/10.3390/bios12050289
https://hdl.handle.net/10578/35130
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 Serrano, JesúsFácila Rubio, LorenzoGracía Baena, Juan ManuelZangroniz Cantabrana, RobertoAlcaraz Martínez, RaúlRieta Ibañez, José JoaquínHigh 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.MDPI202420242022info:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/bios12050289https://hdl.handle.net/10578/35130reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/351302026-05-27T07:36:41Z
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 Serrano, Jesús
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 Serrano, Jesús
Fácila Rubio, Lorenzo
Gracía Baena, Juan Manuel
Zangroniz Cantabrana, Roberto
Alcaraz Martínez, Raúl
Rieta Ibañez, José Joaquín
author Cano Serrano, Jesús
author_facet Cano Serrano, Jesús
Fácila Rubio, Lorenzo
Gracía Baena, Juan Manuel
Zangroniz Cantabrana, Roberto
Alcaraz Martínez, Raúl
Rieta Ibañez, José Joaquín
author_role author
author2 Fácila Rubio, Lorenzo
Gracía Baena, Juan Manuel
Zangroniz Cantabrana, Roberto
Alcaraz Martínez, Raúl
Rieta Ibañez, José Joaquín
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
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.3390/bios12050289
https://hdl.handle.net/10578/35130
url https://doi.org/10.3390/bios12050289
https://hdl.handle.net/10578/35130
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,228081

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