YI 2.1 Pulse Wave Velocity Estimation from the Radial PulseWaveform using Gaussian Process Regression: A Machine Learning Based Study

Weiwei Jin; Phil Chowienczyk; Jordi Alastruey

doi:10.2991/artres.k.201209.009

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Volume 26, Issue Supplement 1, December 2020, Pages S12 - S12

YI 2.1 Pulse Wave Velocity Estimation from the Radial PulseWaveform using Gaussian Process Regression: A Machine Learning Based Study

Authors

Weiwei Jin¹^{, *}, Phil Chowienczyk², Jordi Alastruey¹^{, 3}

¹Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London

²British Heart Foundation Centre, Department of Clinical Pharmacology, St. Thomas’ Hospital, King’s College London

³Institute of Personalized Medicine, Sechenov University

^*Corresponding author. Email: weiwei.jin@kcl.ac.uk

Corresponding Author

Weiwei Jin

Available Online 31 December 2020.

DOI: 10.2991/artres.k.201209.009 How to use a DOI?
Keywords: Vascular ageing; machine learning
Abstract: Objective and Motivation: Pulse wave velocity (PWV) is known to be associated with vascular ageing, a risk factor for cardiovascular disease (CVD) [1]. The European gold standard measurement of PWV requires an experienced operator to measure pulse waveforms at multiple sites, sometimes together with an electrocardiogram [2,3]. This study aims to estimate PWV from the radial pulse waveform using machine learning.

Methods: Radial pulse waveforms and carotid-femoral PWVs were acquired in 3,082 unselected twins (https://twinsuk.ac.uk). 14 fiducial points on each pulse waveform were extracted using an in-house algorithm [4]. LASSO regression and principal component analysis (PCA) were used to identify the key features (timing and magnitude of the fiducial points) associated with PWV and exclude outliers. Finally, Gaussian process regression was used to estimate the PWV based on those key features only.

Results: Results show that PWV can be estimated from the radial pulse waveform only with an overall root mean squared error (RMSE) of 1.82 m/s (Figure A). Most of the measured PWV values were within the 95% confidence interval range of the estimated PWV. The difference between measured and estimated PWV values increased with the increasing PWV. PWV estimation on a subgroup of twins with a healthy range of blood pressure and PWV values [5] was achieved with a RMSE of 1.38 m/s (Figure B).

Conclusion: In this proof-of-concept study we have shown the possibility of estimating PWV from the radial pulse waveform using machine learning. This approach could make CVD detection more accessible to the wider population.
Copyright: © 2020 Association for Research into Arterial Structure and Physiology. Publishing services by Atlantis Press International B.V.
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

REFERENCES

[1]FUS Mattace-Raso, A Hofman, GC Verwoert, JCM Witteman, I Wilkinson, J Cockcroft, et al., Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors: ‘establishing normal and reference values’., Eur Heart J, Vol. 31, 2010, pp. 2338-50.

[2]S Laurent, J Cockcroft, L Van Bortel, P Boutouyrie, C Giannattasio, D Hayoz, et al., Expert consensus document on arterial stiffness: methodological issues and clinical applications, Eur Heart J, Vol. 27, 2006, pp. 2588-605.

[3]LM Van Bortel, S Laurent, P Boutouyrie, P Chowienczyk, JK Cruickshank, T De Backer, et al., Expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity, J Hypertens, Vol. 30, 2012, pp. 445-8.

[4]PH Charlton, JM Harana, S Vennin, Y Li, P Chowienczyk, and J Alastruey, Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of haemodynamics and pulse wave indexes, Am J Physiol Heart Circ Physiol, Vol. 317, 2019, pp. H1062-H85.

[5]A Díaz, C Galli, M Tringler, A Ramírez, and EI Cabrera Fischer, Reference values of pulse wave velocity in healthy people from an urban and rural argentinean population, Int J Hypertens, Vol. 2014, 2014, pp. 653239.

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Journal: Artery Research
Volume-Issue: 26 - Supplement 1
Pages: S12 - S12
Publication Date: 2020/12/31
ISSN (Online): 1876-4401
ISSN (Print): 1872-9312
DOI: 10.2991/artres.k.201209.009 How to use a DOI?
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

ris enw bib

TY  - JOUR
AU  - Weiwei Jin
AU  - Phil Chowienczyk
AU  - Jordi Alastruey
PY  - 2020
DA  - 2020/12/31
TI  - YI 2.1 Pulse Wave Velocity Estimation from the Radial PulseWaveform using Gaussian Process Regression: A Machine Learning Based Study
JO  - Artery Research
SP  - S12
EP  - S12
VL  - 26
IS  - Supplement 1
SN  - 1876-4401
UR  - https://doi.org/10.2991/artres.k.201209.009
DO  - 10.2991/artres.k.201209.009
ID  - Jin2020
ER  -

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