Estimation of central pulse wave velocity from radial pulse wave analysis. (June 2022)

• Record Type: Journal Article
• Authors: Yao, Yang; Zhou, Shuran; Alastruey, Jordi; Hao, Liling; Greenwald, Stephen E.; Zhang, Yuelan; Xu, Lin; Xu, Lisheng; Yao, Yudong
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Arterial stiffness -- Pulse wave analysis -- Pulse wave velocity -- Wave separation analysis; cfPWV carotid-femoral Pulse Wave Velocity -- AIx augmentation index -- WSA wave separation analysis -- SEVR sub-endocardial variability ratio -- RM reflection magnitude -- RI reflection index -- BMI body mass index -- HR heart rate -- ICC intra-class correlation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106781
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: 1 This is the first in vivo study to validate the use of machine learning techniques to improve the accuracy for estimating carotid-femoral pulse wave velocity from a single radial pulse waveform. 2 We found a novel radial pulse feature that correlates with arterial stiffness. 3 We provided a theoretical explanation for this correlation through an in silico study. Abstract: Background and Objective: Arterial stiffness, commonly assessed by carotid-femoral pulse wave velocity (cfPWV), is an independent biomarker for cardiovascular disease. The measurement of cfPWV, however, has been considered impractical for routine clinical application. Pulse wave analysis using a single pulse wave measurement in the radial artery is a convenient alternative. This study aims to identify pulse wave features for a more accurate estimation of cfPWV from a single radial pulse wave measurement. Methods: From a dataset of 140 subjects, cfPWV was measured and the radial pulse waveform was recorded for 30 s twice in succession. Features were extracted from the waveforms in the time and frequency domains, as well as by wave separation analysis. All-possible regressions with bootstrapping, McHenry's select algorithm, and support vector regression were applied to compute models for cfPWV estimation. Results: The correlation coefficients between the measured and estimated cfPWV were r = 0.81, r = 0.81, and r = 0.8 for all-possible regressions, McHenry's select algorithm, and support vector regression, respectively. The features selected by all-possible regressions are physiologically interpretable. In particular, the amplitude ratio of the diastolic peak to the notch of the radial pulse waveform ( R n, d r, P ) is shown to be correlated with cfPWV. This correlation was further evaluated and found to be independent of wave reflections using a dataset ( n = 3, 325) of simulated pulse waves. Conclusions: The proposed method may serve as a convenient surrogate for the measurement of cfPWV. R n, d r, P is associated with aortic pulse wave velocity and this association may not be dependent on wave reflection.