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Estimation of central pulse wave velocity from radial pulse wave analysis

Research output: Contribution to journalArticlepeer-review

Yang Yao, Shuran Zhou, Jordi Alastruey, Liling Hao, Stephen E Greenwald, Yuelan Zhang, Lin Xu, Lisheng Xu, Yudong Yao

Original languageEnglish
Article number106781
JournalComputer Methods and Programs in Biomedicine
Early online date27 Mar 2022
Accepted/In press26 Mar 2022
E-pub ahead of print27 Mar 2022
PublishedJun 2022

Bibliographical note

Funding Information: The authors declare no conflict of interest. This work was supported by the National Natural Science Foundation of China under Grants 61773110 and 61701099 , the Natural Science Foundation of Liaoning Province (No. 20170540312 and No. 2021-YGJC-14 ), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education ( LJKZ00042021 ), the Fundamental Research Funds for the Central Universities (No. N2119008 ). This research was also supported by the Shenyang Science and Technology Plan Fund (No. 21–104–1–24 , No. 20–201–4–10 , and No. 201375 ), the Member Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. (No. MCMP062002). JA acknowledged support from the British Heart Foundation ( PG/15/104/31913), the Cardiovascular MedTech Cooperative at Guy's and St Thomas’ NHS Foundation Trust, and the Ministry of Science and Higher Education of the Russian Federation ( 075–15–2020- 926 ). Publisher Copyright: © 2022 Elsevier B.V.

King's Authors


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.

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