Dataset of Pulse Waves for Thousands of Virtual Subjects, aged 55-75 years old, with/out AAAs
The full database can be downloaded from here. This repository also contains all algorithms used to create the database and for subsequent pulse wave analysis, including a machine learning model for detecting abdominal aortic aneurysms (AAAs) by pulse wave analysis.

Using Nektar1D, we created a database of 10,935 virtual subjects aged 55, 65 and 75 years old, each with a distinctive set of arterial pulse waveforms. For each subject, arterial blood pressure (P), flow rate (Q), flow velocity (U), luminal area (A), and photoplethysmogram (PPG) pulse waves are available at a range of measurement sites, together with the parameters of the simulation (e.g. vessel geometries, AAA size, cardiac output, arterial stiffness). These waves were simulated at baseline (normal physiology), with increased global stiffness characteristic of subjects with AAAs, and with different AAA sizes.
Tianqi Wang, Weiwei Jin, Fuyou Liang and Jordi Alastruey. Machine learning-based pulse wave analysis for early detection of abdominal aortic aneurysms using in silico pulse waves. Symmetry 13(5):804, 2021
An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, the early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging only incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs—identified by parameter sensitivity analysis—in an existing database of in silico PWs representative of subjects without AAAs. Then, a machine learning architecture for AAA detection was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties considerably influencing the PWs. However, AAA detection by PW indexes was compromised by other non-AAA related cardiovascular parameters. The proposed machine learning model produced a sensitivity of 86.8 % and a specificity of 86.3 % in early detection of AAA from the photoplethysmogram PW signal measured in the digital artery with added random noise. The number of false positive and negative results increased with increasing age and decreasing AAA size, respectively. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices..


The database was verified by comparing the simulated pulse waves and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in haemodynamic parameters and pulse wave morphology. Simulation of pulse wave propagation in vessels with aneurysms has been tested in J Royal Soc Interface, 2021.

The database was used to provide a proof of concept for the feasibility of early detection of AAAs by machine learning-based analysis of PPG waves. Given the high mortality of AAA rupture, early detection of AAAs is crucial for an effective treatment to reduce the risk of rupture. The proliferation of commercial wearable devices that can accurately measure PPG signals on the wrist or finger offers an opportunity to screen the wider population for AAAs in daily life.
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