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Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram

Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare to non-hospital settings. Most of the current research has focused on the estimation of blood pressure based on pulse transit time or pulse arrival time...

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Autores principales: Gonzalez-Landaeta, Rafael, Ramirez, Brenda, Mejia, Jose
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562413/
https://www.ncbi.nlm.nih.gov/pubmed/36229644
http://dx.doi.org/10.1038/s41598-022-22205-0
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author Gonzalez-Landaeta, Rafael
Ramirez, Brenda
Mejia, Jose
author_facet Gonzalez-Landaeta, Rafael
Ramirez, Brenda
Mejia, Jose
author_sort Gonzalez-Landaeta, Rafael
collection PubMed
description Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare to non-hospital settings. Most of the current research has focused on the estimation of blood pressure based on pulse transit time or pulse arrival time using ECG or peripheral cardiac pulse signals as proximal time references. This study proposed the use of a phonocardiogram (PCG) and ballistocardiogram (BCG), two signals detected noninvasively, to estimate systolic blood pressure (SBP). For this, the PCG and the BCG were simultaneously measured in 21 volunteers in the rest, activity, and post-activity conditions. Different features were considered based on the relationships between these signals. The intervals between S1 and S2 of the PCG and the I, J, and K waves of the BCG were statistically analyzed. The IJ and JK slopes were also estimated as additional features to train the machine-learning algorithm. The intervals S1-J, S1-K, S1-I, J-S2, and I-S2 were negatively correlated with changes in SBP (p-val < 0.01). The features were used as explanatory variables for a regressor based on the Random Forest. It was possible to estimate the systolic blood pressure with a mean error of 3.3 mmHg with a standard deviation of ± 5 mmHg. Therefore, we foresee that this proposal has potential applications for wearable devices that use low-cost embedded systems.
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spelling pubmed-95624132022-10-15 Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram Gonzalez-Landaeta, Rafael Ramirez, Brenda Mejia, Jose Sci Rep Article Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare to non-hospital settings. Most of the current research has focused on the estimation of blood pressure based on pulse transit time or pulse arrival time using ECG or peripheral cardiac pulse signals as proximal time references. This study proposed the use of a phonocardiogram (PCG) and ballistocardiogram (BCG), two signals detected noninvasively, to estimate systolic blood pressure (SBP). For this, the PCG and the BCG were simultaneously measured in 21 volunteers in the rest, activity, and post-activity conditions. Different features were considered based on the relationships between these signals. The intervals between S1 and S2 of the PCG and the I, J, and K waves of the BCG were statistically analyzed. The IJ and JK slopes were also estimated as additional features to train the machine-learning algorithm. The intervals S1-J, S1-K, S1-I, J-S2, and I-S2 were negatively correlated with changes in SBP (p-val < 0.01). The features were used as explanatory variables for a regressor based on the Random Forest. It was possible to estimate the systolic blood pressure with a mean error of 3.3 mmHg with a standard deviation of ± 5 mmHg. Therefore, we foresee that this proposal has potential applications for wearable devices that use low-cost embedded systems. Nature Publishing Group UK 2022-10-13 /pmc/articles/PMC9562413/ /pubmed/36229644 http://dx.doi.org/10.1038/s41598-022-22205-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gonzalez-Landaeta, Rafael
Ramirez, Brenda
Mejia, Jose
Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title_full Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title_fullStr Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title_full_unstemmed Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title_short Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
title_sort estimation of systolic blood pressure by random forest using heart sounds and a ballistocardiogram
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562413/
https://www.ncbi.nlm.nih.gov/pubmed/36229644
http://dx.doi.org/10.1038/s41598-022-22205-0
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