A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susc...

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Autores principales:	Zheming Li, Wei He
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	blood pressure waveform photoplethysmogram neural network blood pressure estimation harmonic Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/b26502342d6a4348ab0cb6465819dd60
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id	oai:doaj.org-article:b26502342d6a4348ab0cb6465819dd60
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spelling	oai:doaj.org-article:b26502342d6a4348ab0cb6465819dd602021-11-11T19:11:25ZA Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model10.3390/s212172071424-8220https://doaj.org/article/b26502342d6a4348ab0cb6465819dd602021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7207https://doaj.org/toc/1424-8220Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.Zheming LiWei HeMDPI AGarticleblood pressure waveformphotoplethysmogramneural networkblood pressure estimationharmonicChemical technologyTP1-1185ENSensors, Vol 21, Iss 7207, p 7207 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	blood pressure waveform photoplethysmogram neural network blood pressure estimation harmonic Chemical technology TP1-1185
spellingShingle	blood pressure waveform photoplethysmogram neural network blood pressure estimation harmonic Chemical technology TP1-1185 Zheming Li Wei He A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
description	Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
format	article
author	Zheming Li Wei He
author_facet	Zheming Li Wei He
author_sort	Zheming Li
title	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
title_short	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
title_full	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
title_fullStr	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
title_full_unstemmed	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
title_sort	continuous blood pressure estimation method using photoplethysmography by grnn-based model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b26502342d6a4348ab0cb6465819dd60
work_keys_str_mv	AT zhemingli acontinuousbloodpressureestimationmethodusingphotoplethysmographybygrnnbasedmodel AT weihe acontinuousbloodpressureestimationmethodusingphotoplethysmographybygrnnbasedmodel AT zhemingli continuousbloodpressureestimationmethodusingphotoplethysmographybygrnnbasedmodel AT weihe continuousbloodpressureestimationmethodusingphotoplethysmographybygrnnbasedmodel
_version_	1718431605349941248

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

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