Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.

One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to es...

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Autores principales: Weiwei Jin, Philip Chowienczyk, Jordi Alastruey
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/8b0c22e681634ddfab69f8477163370c
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spelling oai:doaj.org-article:8b0c22e681634ddfab69f8477163370c2021-12-02T20:09:56ZEstimating pulse wave velocity from the radial pressure wave using machine learning algorithms.1932-620310.1371/journal.pone.0245026https://doaj.org/article/8b0c22e681634ddfab69f8477163370c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0245026https://doaj.org/toc/1932-6203One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).Weiwei JinPhilip ChowienczykJordi AlastrueyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0245026 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Weiwei Jin
Philip Chowienczyk
Jordi Alastruey
Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
description One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).
format article
author Weiwei Jin
Philip Chowienczyk
Jordi Alastruey
author_facet Weiwei Jin
Philip Chowienczyk
Jordi Alastruey
author_sort Weiwei Jin
title Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
title_short Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
title_full Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
title_fullStr Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
title_full_unstemmed Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
title_sort estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/8b0c22e681634ddfab69f8477163370c
work_keys_str_mv AT weiweijin estimatingpulsewavevelocityfromtheradialpressurewaveusingmachinelearningalgorithms
AT philipchowienczyk estimatingpulsewavevelocityfromtheradialpressurewaveusingmachinelearningalgorithms
AT jordialastruey estimatingpulsewavevelocityfromtheradialpressurewaveusingmachinelearningalgorithms
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