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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9562413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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