ArticlePLoS computational biology2019
Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 53 citations in OpenAlex.
- Classification of patients with Alzheimer's disease using the arterial pulse spectrum and a multilayer-perceptron analysis.Scientific reports · 2021Trial
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- Synthetic, Population-Based Virtual Patient Database Using a Digital Twin of the Cardiovascular System.Cardiovascular engineering and technology · 2026Article
- Heart-retina time analysis using electrocardiogram-coupled time-resolved dynamic optical coherence tomography.Scientific reports · 2025Article
- Simultaneous Measurement of Local Pulse Wave Velocities in Radial Arteries Using a Soft Sensor Based on the Fiber Bragg Grating Technique.Micromachines · 2024Article
- Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system.American journal of physiology. Heart and circulatory physiology · 2023Article
- Survey and Evaluation of Hypertension Machine Learning Research.Journal of the American Heart Association · 2023Article
- Machine-Learning Classification of Pulse Waveform Quality.Sensors (Basel, Switzerland) · 2022Article
- Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.European heart journal. Digital health · 2021Article
- Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography.Human brain mapping · 2021Article
- Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: AnFrontiers in bioengineering and biotechnology · 2021Article
- [Application of biomechanical modeling and simulation in the development of non-invasive technologies and devices for cardiovascular testing].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring.Sensors (Basel, Switzerland) · 2020Article
- Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of virtual subjects, with sizes limited only by computational resources. In this work, we study if it is possible to estimate cardiovascular health indices using machine learning approaches. In particular, we carry out theoretical assessment of estimating aortic pulse wave velocity, diastolic and systolic blood pressure and stroke volume using pulse transit/arrival timings derived from photopletyshmography signals. For predictions, we train Gaussian process regression using a database of virtual subjects generated with a cardiovascular simulator. Simulated results provides theoretical assessment of accuracy for predictions of the health indices. For instance, aortic pulse wave velocity can be estimated with a high accuracy (r > 0.9) when photopletyshmography is measured from left carotid artery using a combination of foot-to-foot pulse transmit time and peak location derived for the predictions. Similar accuracy can be reached for diastolic blood pressure, but predictions of systolic blood pressure are less accurate (r > 0.75) and the stroke volume predictions are mostly contributed by heart rate.
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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 OpenQuestion graph.