A Computational Approach for Assessing Haemodynamics and Cardiovascular Indices and Algorithms
Pulse wave signals such as blood pressure, flow and PPG waves contain a wealth of information on the cardiovascular system, since they are influenced by both the heart and the vasculature. Consequently, many indices and algorithms have been proposed to infer the physiological state of the cardiovascular system by analysing pulse waveforms.
Acquiring comprehensive datasets for assessing the performance of these indices and algorithms is usually a complex task: it can be difficult to measure reference variables precisely (e.g. cardiac output); it can be difficult to measure pulse waves at all the sites of interest (particularly central arteries); clinical trials are expensive and time-consuming, and in vivo measurements are subject to experimental errors.
Here we provide a database of simulated arterial pulse waves to perform this task in silico. Using Nektar1D, we have created a database of over 3,325 virtual subjects, each with distinctive arterial pulse waveforms.
For each subject, blood pressure, blood flow and luminal area waveforms are available at multiple arterial locations, together with the parameters of the simulation (e.g. vessel geometries, cardiac output, pulse wave velocities). Below we provide links to download the full dataset together with Matlab tools for processing and analysing the data.
The following figure presents an example of simultaneous pressure and flow waves at several locations in the arterial network for one virtual subject. The model is able to reproduce the main waveform features observed in vivo, including amplification of the pulse pressure distal to the heart, positive carotid flow, and presence of backflow in early diastole in the leg arteries. The aortic pressure wave presents an inflection point located before the systolic peak; waveforms are therefore of type-A according to Murgo's classification, which are representative of an older age category.
In our initial study (Am J Phys, 2015), we used the database to assess the accuracy of pulse wave indices of aortic pulse wave velocity (PWV). This is a surrogate for aortic stiffness which is an important marker of vascular health.
Marie Willemet, Phil Chowienczyk and Jordi Alastruey. A database of virtual healthy subjects to assess the accuracy of foot-to-foot pulse wave velocities for estimation of aortic stiffness.American Journal of Physiology: Heart and Circulatory Physiology, 309:H663-675, 2015
While central (carotid-femoral) foot-to-foot pulse wave velocity (PWV) is considered to be the gold standard for the estimation of aortic arterial stiffness, peripheral foot-to-foot PWV (brachial-ankle, femoral-ankle, carotid-radial) are being studied as substitutes of this central measurement. We present a novel methodology to assess theoretically these computed indices and the hemodynamics mechanisms relating them. We created a database of 3320 virtual healthy adult subjects using a validated one-dimensional model of the arterial hemodynamics, with cardiac and arterial parameters varied within physiological healthy ranges. For each virtual subject, foot-to-foot PWV were computed from numerical pressure waveforms at the same locations where clinical measurements are commonly taken. Our results confirm clinical observations: (i) carotid-femoral PWV is a good indicator of aortic stiffness, and correlates well with aortic PWV; (ii) brachial-ankle PWV over-estimates aortic PWV and is related to the stiffness and geometry of both elastic and muscular arteries; (iii) muscular PWV (carotid-radial, femoral-ankle) do not capture the stiffening of the aorta and should therefore not be used as a surrogate for aortic stiffness. In addition, our analysis highlights that the foot-to-foot PWV algorithm is sensitive to the presence of reflected waves in late diastole, which introduce errors in the PWV estimates. In this study, we have created a database of virtual healthy subjects which can be used to assess theoretically the efficiency of physiological indices based on pulse wave analysis.
More recently, the database has been used to produce algorithms for non-invasive estimation of
(i) local arterial wave speed and mean blood velocity waveforms
using Doppler ultrasound (Physiol Measur, 2017);
(ii) central blood pressure (Hypertension, 2017; J Biomech, 2016).
Forward and backward pressure waveform morphology in hypertension has also been studied using virtual subjects (Hypertension, 2017).
The algorithms used in all these studies were first developed and tested using virtual subjects and then assessed in vivo using cohorts of real subjects.
Download the database
The complete database can be downloaded using the links below. Data is sorted by arterial location and saved in Matlab formatted files of about 300 MB each. The structure of the database is explained in detail in the reference manual. Subjects with pulse waveforms that did not satisfy the filter criteria for pysiological values of blood pressure and reflection coefficients are also provided for completness (under the 'Non-Physiological data' column). The Fictive_database.mat file contains a description of the arterial network geometry, and values of computed haemodynamic indices (e.g. cardiac output, pulse wave velocity, pulse pressure). We also provide search tools (Matlab functions) which return subjects from the database with specific parameters and computed indices.