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Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology

Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertai...

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Autores principales: Lee, Soojeong, Dajani, Hilmi R, Rajan, Sreeraman, Lee, Gangseong, Groza, Voicu Z
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180780/
https://www.ncbi.nlm.nih.gov/pubmed/32276502
http://dx.doi.org/10.3390/s20072108
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author Lee, Soojeong
Dajani, Hilmi R
Rajan, Sreeraman
Lee, Gangseong
Groza, Voicu Z
author_facet Lee, Soojeong
Dajani, Hilmi R
Rajan, Sreeraman
Lee, Gangseong
Groza, Voicu Z
author_sort Lee, Soojeong
collection PubMed
description Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.
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spelling pubmed-71807802020-05-01 Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology Lee, Soojeong Dajani, Hilmi R Rajan, Sreeraman Lee, Gangseong Groza, Voicu Z Sensors (Basel) Article Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage. MDPI 2020-04-08 /pmc/articles/PMC7180780/ /pubmed/32276502 http://dx.doi.org/10.3390/s20072108 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Soojeong
Dajani, Hilmi R
Rajan, Sreeraman
Lee, Gangseong
Groza, Voicu Z
Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title_full Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title_fullStr Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title_full_unstemmed Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title_short Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology
title_sort uncertainty in blood pressure measurement estimated using ensemble-based recursive methodology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180780/
https://www.ncbi.nlm.nih.gov/pubmed/32276502
http://dx.doi.org/10.3390/s20072108
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