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Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model

Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with...

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Detalles Bibliográficos
Autores principales: Wang, Qian, Xu, Yajie, Zeng, Guoqiang, Sun, Mingshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311810/
https://www.ncbi.nlm.nih.gov/pubmed/30647768
http://dx.doi.org/10.1155/2018/1981627
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author Wang, Qian
Xu, Yajie
Zeng, Guoqiang
Sun, Mingshan
author_facet Wang, Qian
Xu, Yajie
Zeng, Guoqiang
Sun, Mingshan
author_sort Wang, Qian
collection PubMed
description Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with a pressure sensor. Method. The optimal external pressure applied on the pressure sensor is first determined in order to capture pulse wave in the radial artery. The captured pulse wave is then processed in both the time and frequency domains via filtering and fast Fourier transform. Finally, a set of features are extracted from these two domains and input into a neural network along with blood pressure values measured by a commercial sphygmomanometer for training. The model is then tested on new data for accuracy evaluation. Results. The proposed two-domain fusion method achieved a high degree of accuracy in measuring blood pressure.
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spelling pubmed-63118102019-01-15 Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model Wang, Qian Xu, Yajie Zeng, Guoqiang Sun, Mingshan Comput Math Methods Med Research Article Blood pressure (BP) is one of the indispensable elements of physiological health characteristics and a significant indicator for predicting and diagnosing hypertension and cardiovascular diseases. This paper proposes a two-domain fusion model to estimate BP continuously from pulse wave acquired with a pressure sensor. Method. The optimal external pressure applied on the pressure sensor is first determined in order to capture pulse wave in the radial artery. The captured pulse wave is then processed in both the time and frequency domains via filtering and fast Fourier transform. Finally, a set of features are extracted from these two domains and input into a neural network along with blood pressure values measured by a commercial sphygmomanometer for training. The model is then tested on new data for accuracy evaluation. Results. The proposed two-domain fusion method achieved a high degree of accuracy in measuring blood pressure. Hindawi 2018-12-16 /pmc/articles/PMC6311810/ /pubmed/30647768 http://dx.doi.org/10.1155/2018/1981627 Text en Copyright © 2018 Qian Wang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Qian
Xu, Yajie
Zeng, Guoqiang
Sun, Mingshan
Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title_full Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title_fullStr Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title_full_unstemmed Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title_short Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
title_sort continuous blood pressure estimation based on two-domain fusion model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311810/
https://www.ncbi.nlm.nih.gov/pubmed/30647768
http://dx.doi.org/10.1155/2018/1981627
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