ArticleScientific reports2018
Estimation of Cardiovascular Risk Predictors from Non-Invasively Measured Diametric Pulse Volume Waveforms via Multiple Measurement Information Fusion.
Article in Scientific reports, 2018. 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.
- Transmission line model as a digital twin for abdominal aortic aneurysm patients.NPJ digital medicine · 2024Article
- Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms.Computers in biology and medicine · 2024Article
- Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors.IEEE open journal of engineering in medicine and biology · 2023Article
- Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference.PLoS computational biology · 2022Article
- Sparse System Identification of Leptin Dynamics in Women With Obesity.Frontiers in endocrinology · 2022Article
- Deep Learning-Based Diagnosis of Peripheral Artery Disease via Continuous Property-Adversarial Regularization: Preliminary in Silico Study.IEEE access : practical innovations, open solutions · 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
- Conventional pulse transit times as markers of blood pressure changes in humans.Scientific reports · 2020Article
- Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.Scientific reports · 2020Article
- Observer-Based Deconvolution of Deterministic Input in Coprime Multichannel Systems With Its Application to Noninvasive Central Blood Pressure Monitoring.Journal of dynamic systems, measurement, and control · 2020Article
- Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges.Frontiers in bioengineering and biotechnology · 2020Article
- Multiscale mathematical modeling vs. the generalized transfer function approach for aortic pressure estimation: a comparison with invasive data.Hypertension research : official journal of the Japanese Society of Hypertension · 2019Article
- A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure.Biomedical engineering online · 2019Review
- Tapered vs. Uniform Tube-Load Modeling of Blood Pressure Wave Propagation in Human Aorta.Frontiers in physiology · 2019Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a novel multiple measurement information fusion approach to the estimation of cardiovascular risk predictors from non-invasive pulse volume waveforms measured at the body's diametric (arm and ankle) locations. Leveraging the fact that diametric pulse volume waveforms originate from the common central pulse waveform, the approach estimates cardiovascular risk predictors in three steps by: (1) deriving lumped-parameter models of the central-diametric arterial lines from diametric pulse volume waveforms, (2) estimating central blood pressure waveform by analyzing the diametric pulse volume waveforms using the derived arterial line models, and (3) estimating cardiovascular risk predictors (including central systolic and pulse pressures, pulse pressure amplification, and pulse transit time) from the arterial line models and central blood pressure waveform in conjunction with the diametric pulse volume waveforms. Experimental results obtained from 164 human subjects with a wide blood pressure range (systolic 144 mmHg and diastolic 103 mmHg) showed that the approach could estimate cardiovascular risk predictors accurately (r ≥ 0.78). Further analysis showed that the approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification. The approach may be integrated with already available medical devices to enable convenient out-of-clinic cardiovascular risk prediction.
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