Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram

Continuous cuff-less blood pressure (BP) monitoring has become a research hotspot in recent years. Researches have studied the impact of pulse transit time (PTT) and ballistocardiogram (BCG) signals on BP. However, the accuracy of these methods are not high enough to put them into practice on a larg...

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Autores principales:	Yandong Zhang, Xianwen Zhang, Pengfei Cui, Shuo Li, Jintian Tang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Ballistocardiogram cuff-less blood pressure monitoring electrocardiogram photoplethysmogram Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6f9ca63c9b4d4caf82c580015d1c8034
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spelling	oai:doaj.org-article:6f9ca63c9b4d4caf82c580015d1c80342021-11-19T00:06:05ZKey Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram2169-353610.1109/ACCESS.2021.3070636https://doaj.org/article/6f9ca63c9b4d4caf82c580015d1c80342021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9393972/https://doaj.org/toc/2169-3536Continuous cuff-less blood pressure (BP) monitoring has become a research hotspot in recent years. Researches have studied the impact of pulse transit time (PTT) and ballistocardiogram (BCG) signals on BP. However, the accuracy of these methods are not high enough to put them into practice on a large scale. In this paper, we propose a new BP estimation model which combines features extracted from electrocardiogram (ECG), BCG and photoplethysmogram (PPG). We calculate several features containing amplitude, time and energy from these three signals and use stepwise regression to select key ones for this combination model. The combination model was examined in 20 young healthy subjects and it presents good results: a correlation coefficient (R) of 0.84 (systolic blood pressure, SBP) and 0.7 (diastolic blood pressure, DBP), a root-mean-squared error (RMSE) of 8.16 mmHg (SBP) and 6.63 mmHg (DBP), and a mean absolute error (MAE) of 6.84 mmHg (SBP) and 5.46 mmHg (SBP). Besides, The PTT-based BP estimation model and BCG-based estimation model are also established in this paper. The comparison of these three models shows that the PPG-ECG-BCG-based model has better performance.Yandong ZhangXianwen ZhangPengfei CuiShuo LiJintian TangIEEEarticleBallistocardiogramcuff-less blood pressure monitoringelectrocardiogramphotoplethysmogramElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 54350-54359 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ballistocardiogram cuff-less blood pressure monitoring electrocardiogram photoplethysmogram Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Ballistocardiogram cuff-less blood pressure monitoring electrocardiogram photoplethysmogram Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yandong Zhang Xianwen Zhang Pengfei Cui Shuo Li Jintian Tang Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
description	Continuous cuff-less blood pressure (BP) monitoring has become a research hotspot in recent years. Researches have studied the impact of pulse transit time (PTT) and ballistocardiogram (BCG) signals on BP. However, the accuracy of these methods are not high enough to put them into practice on a large scale. In this paper, we propose a new BP estimation model which combines features extracted from electrocardiogram (ECG), BCG and photoplethysmogram (PPG). We calculate several features containing amplitude, time and energy from these three signals and use stepwise regression to select key ones for this combination model. The combination model was examined in 20 young healthy subjects and it presents good results: a correlation coefficient (R) of 0.84 (systolic blood pressure, SBP) and 0.7 (diastolic blood pressure, DBP), a root-mean-squared error (RMSE) of 8.16 mmHg (SBP) and 6.63 mmHg (DBP), and a mean absolute error (MAE) of 6.84 mmHg (SBP) and 5.46 mmHg (SBP). Besides, The PTT-based BP estimation model and BCG-based estimation model are also established in this paper. The comparison of these three models shows that the PPG-ECG-BCG-based model has better performance.
format	article
author	Yandong Zhang Xianwen Zhang Pengfei Cui Shuo Li Jintian Tang
author_facet	Yandong Zhang Xianwen Zhang Pengfei Cui Shuo Li Jintian Tang
author_sort	Yandong Zhang
title	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
title_short	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
title_full	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
title_fullStr	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
title_full_unstemmed	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
title_sort	key feature selection and model analysis for blood pressure estimation from electrocardiogram, ballistocardiogram and photoplethysmogram
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/6f9ca63c9b4d4caf82c580015d1c8034
work_keys_str_mv	AT yandongzhang keyfeatureselectionandmodelanalysisforbloodpressureestimationfromelectrocardiogramballistocardiogramandphotoplethysmogram AT xianwenzhang keyfeatureselectionandmodelanalysisforbloodpressureestimationfromelectrocardiogramballistocardiogramandphotoplethysmogram AT pengfeicui keyfeatureselectionandmodelanalysisforbloodpressureestimationfromelectrocardiogramballistocardiogramandphotoplethysmogram AT shuoli keyfeatureselectionandmodelanalysisforbloodpressureestimationfromelectrocardiogramballistocardiogramandphotoplethysmogram AT jintiantang keyfeatureselectionandmodelanalysisforbloodpressureestimationfromelectrocardiogramballistocardiogramandphotoplethysmogram
_version_	1718420614870466560

Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram

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