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Updated: Jan 20, 2026

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging
Published on: February 7, 2014
Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave
Peter H Charlton1, Jorge Mariscal Harana1, Samuel Vennin1,2
1Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.
Insights
A new computer-simulated pulse wave (PW) database aids cardiovascular health research. This resource helps understand PW determinants and develop analysis algorithms for better cardiovascular assessments.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Computational modeling
Background:
- Arterial pulse waves (PWs) offer insights into cardiovascular (CV) health but their physical determinants are not fully understood.
- Current development of PW analysis algorithms is hindered by a lack of comprehensive PW datasets with reference CV measurements.
Purpose of the Study:
- To create a computational database of simulated PWs representing a range of CV conditions in healthy adults aged 25-75.
- To provide a resource for understanding PW determinants and advancing PW analysis algorithm development.
Main Methods:
- A literature review identified typical CV properties across age decades (25-75 years).
- A computational model simulated PWs (pressure, flow velocity, luminal area, photoplethysmographic) using identified CV properties.
- A database of 4,374 virtual subjects' PWs was generated and validated against in vivo data.
Main Results:
- Simulated PWs and derived indexes showed good agreement with in vivo data, accurately reproducing age-related hemodynamic and morphological changes.
- The database facilitated novel hemodynamic insights and in silico assessment of PW algorithms.
- Case studies demonstrated the database's utility in understanding PW index determinants.
Conclusions:
- The publicly available PW database is a valuable resource for CV research.
- It supports the understanding of PW determinants and the development and preclinical assessment of PW analysis algorithms.
- The known CV properties for each simulated PW enhance its utility for algorithm development and validation.
Abstract:
The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25-75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known.NEW & NOTEWORTHY First, a comprehensive literature review of changes in cardiovascular properties with age was performed. Second, an approach for simulating pulse waves (PWs) at different ages was designed and verified against in vivo data. Third, a PW database was created, and its utility was illustrated through three case studies investigating the determinants of PW indexes. Fourth, the database and tools for creating the database, analyzing PWs, and replicating the case studies are freely available.
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