Datasets of In Silico Pulse Waves for Thousands of Virtual Subjects

Pulse wave signals such as blood pressure and PPG waves are used for physiological assessment in both clinical medicine and consumer devices (e.g. smartwatches). They are influenced by the heart, vasculature, and respiratory and autonomic nervous systems, making them a rich source of information on cardiovascular function. Consequently, many indices and algorithms have been proposed to infer the physiological state of the cardiovascular system by analysing pulse wave morphology.

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 is challenging to study the influence of individual cardiovascular properties on the pulse wave in vivo since other properties may change concurrently; it can be complex 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 error.

To facilitate this process, we have created datasets of simulated pulse waves representative of samples of real subjects using computational blood flow modelling. This is a novel and cost-effective approach for the development and pre-clinical testing of pulse wave analysis algorithms across a wide range of cardiovascular conditions, in a relatively quick and inexpensive manner. In silico pulse wave databases also enable us to understand physical mechanisms underlying correlations observed from populations of real subjects and train machine-learning algorithms for pulse wave analysis across a wide range of physiological conditions.

All the datasets created up to now - together with Matlab functions for processing and analysing the data - can be downloaded by clicking the above buttons.