ArticlePloS one2021
Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions.Frontiers in artificial intelligence · 2026Pooled it
- Protocol for a Radial Pulse-Based Vascular Age Index Integrated with Traditional Korean Medicine Pulse Diagnosis.Journal of pharmacopuncture · 2026Article
- Multimodal deep learning for cardiovascular disease detection using pulse wave and vocal signals: a prediction model development and validation study.BMC medical informatics and decision making · 2026Article
- [A multi-source information fusion-based method for the local pulse wave velocity estimation in carotid artery].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Pulse Wave Velocity Estimation in a Controlled In Vitro Vascular Model: Benchmarking Machine Learning Approaches.Sensors (Basel, Switzerland) · 2026Article
- Article
- The relationship between estimated pulse wave velocity and 28-day mortality in patients with sA-AKI: a retrospective cohort analysis of the MIMIC-IV database.Renal failure · 2025Article
- Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.Biomedical engineering online · 2025Article
- Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population.Scientific reports · 2025Article
- Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.Physiological measurement · 2024Review
- Article
- Association between estimated pulse wave velocity and in-hospital mortality of patients with acute kidney injury: a retrospective cohort analysis of the MIMIC-IV database.Renal failure · 2024Article
- Update on the Use of Pulse Wave Velocity to Measure Age-Related Vascular Changes.Current hypertension reports · 2024Review
- Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning.Frontiers in cardiovascular medicine · 2024Article
- Association between serum Klotho levels and estimated pulse wave velocity in postmenopausal women: a cross-sectional study of NHANES 2007-2016.Frontiers in endocrinology · 2024Article
- 2024 Recommendations for Validation of Noninvasive Arterial Pulse Wave Velocity Measurement Devices.Hypertension (Dallas, Tex. : 1979) · 2024Article
- Noninvasive hemodynamic indices of vascular aging: an in silico assessment.American journal of physiology. Heart and circulatory physiology · 2023Article
- Use of a rat model to characterize 35 arterial pulse wave parameters in a comparative study of isoflurane and Zoletil/xylazine anesthesia and the effect of Acanthopanax senticosus extract.Animal models and experimental medicine · 2023Article
- Non-Invasive Pressure-Only Aortic Wave Intensity Evaluation Using Hybrid Fourier Decomposition-Machine Learning Approach.IEEE transactions on bio-medical engineering · 2023Article
- The Phthalic Selenoanhydride Decreases Rat Blood Pressure and Tension of Isolated Mesenteric, Femoral and Renal Arteries.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.