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

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

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dc.contributor.author Cano, Jesús es_ES
dc.contributor.author Fácila, Lorenzo es_ES
dc.contributor.author Gracia-Baena, Juan M. es_ES
dc.contributor.author Zangróniz, Roberto es_ES
dc.contributor.author Alcaraz, Raúl es_ES
dc.contributor.author Rieta, J J es_ES
dc.date.accessioned 2022-11-23T19:01:14Z
dc.date.available 2022-11-23T19:01:14Z
dc.date.issued 2022-05 es_ES
dc.identifier.issn 2079-6374 es_ES
dc.identifier.uri http://hdl.handle.net/10251/190113
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship This research received financial support from grants PID2021-00X128525-IV0, PID2021123804OB-I00 and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Biosensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject High blood pressure es_ES
dc.subject Hypertension es_ES
dc.subject Photoplethysmography es_ES
dc.subject Electrocardiography es_ES
dc.subject Calibration es_ES
dc.subject Classification models es_ES
dc.subject Machine learning es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/bios12050289 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2021%2F286//Inteligencia Artificial para Revolucionar la Medicina Móvil Usando Dispositivos Llevables/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//PID2021-00X128525-IV0/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//TED2021-129996B-I00/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//PID2021-123804OB-I00/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.description.bibliographicCitation Cano, J.; Fácila, L.; Gracia-Baena, JM.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2022). The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography. Biosensors. 12(5):1-14. https://doi.org/10.3390/bios12050289 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/bios12050289 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 5 es_ES
dc.identifier.pmid 35624590 es_ES
dc.identifier.pmcid PMC9138834 es_ES
dc.relation.pasarela S\463414 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Junta de Comunidades de Castilla-La Mancha es_ES
upv.costeAPC 1623,08 es_ES


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