A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable intere...

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Detalles Bibliográficos
Autores principales: Wang, Ludi, Zhou, Wei, Xing, Ying, Zhou, Xiaoguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863309/
https://www.ncbi.nlm.nih.gov/pubmed/29707186
http://dx.doi.org/10.1155/2018/7804243
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author Wang, Ludi
Zhou, Wei
Xing, Ying
Zhou, Xiaoguang
author_facet Wang, Ludi
Zhou, Wei
Xing, Ying
Zhou, Xiaoguang
author_sort Wang, Ludi
collection PubMed
description The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.
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spelling pubmed-58633092018-04-29 A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram Wang, Ludi Zhou, Wei Xing, Ying Zhou, Xiaoguang J Healthc Eng Research Article The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP. Hindawi 2018-03-07 /pmc/articles/PMC5863309/ /pubmed/29707186 http://dx.doi.org/10.1155/2018/7804243 Text en Copyright © 2018 Ludi Wang et al. http://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, Ludi
Zhou, Wei
Xing, Ying
Zhou, Xiaoguang
A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title_full A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title_fullStr A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title_full_unstemmed A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title_short A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
title_sort novel neural network model for blood pressure estimation using photoplethesmography without electrocardiogram
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863309/
https://www.ncbi.nlm.nih.gov/pubmed/29707186
http://dx.doi.org/10.1155/2018/7804243
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