Estimation of central pulse wave velocity from radial pulse wave analysis

• Published in: Computer methods and programs in biomedicine Vol. 219; p. 106781
• Main Authors: Yao, Yang, Zhou, Shuran, Alastruey, Jordi, Hao, Liling, Greenwald, Stephen E., Zhang, Yuelan, Xu, Lin, Xu, Lisheng, Yao, Yudong
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2022
• Subjects: Arterial stiffness; Blood Pressure; Carotid Arteries - physiology; Carotid-Femoral Pulse Wave Velocity; Humans; Pulse wave analysis; Pulse Wave Analysis - methods; Pulse wave velocity; Radial Artery; Wave separation analysis; Pulse wave analysis; Arterial stiffness; WSA; Wave separation analysis; SEVR; ICC; HR; cfPWV; AIx; RI; Pulse wave velocity; RM; BMI
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106781

1This 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.2We found a novel radial pulse feature that correlates with arterial stiffness.3We provided a theoretical explanation for this correlation through an in silico study. 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 (Rn,dr,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. Rn,dr,P is associated with aortic pulse wave velocity and this association may not be dependent on wave reflection.