Catalyst Awardee

Project Description

Continuous Monitoring of Vascular Age by Pulse Wave Velocity Using Wearable ECG and PPG Sensors

Professor Christian Heiss | University of Surrey; Dr. Radu Sporea; Professor Philip Aston; Dr. David Birch; Professor Simon Skene
Competition Sponsor: UK Research & Innovation
Awardee Year: 2020

While healthy ageing is one of the most important challenges of our time, it is difficult to monitor ageing during normal life. The ability to continuously measure ageing with low cost devices would open opportunities to test interventions that aim to slow ageing in a large number of individual people.

Progressive stiffening of the blood vessels is one of the most important features of human ageing. Importantly, arterial stiffness can be measured non-invasively as pulse wave velocity (PWV) and is viewed as the most important biomarker of vascular age, but it currently requires costly equipment and trained researchers to measure. However, personal devices like fitness bracelets and smart watches are now able to measure a variety of biological information including blood flow and electrical activity of the heart. This information could be used to calculate PWV with each heartbeat in real time and provide immediate information on the biological age of the blood vessels. More importantly, this approach would allow the identification of factors that accelerate and slow ageing in an individual person and monitor the success of “anti-ageing” therapies in an individual person. So far, no technology is available that fulfills this task.

In the current project, we wish to develop low-cost technology either using existing devices or build prototype device with available components to allow monitoring of individual “vascular age” at scale.

In the future, we hope to be able to test the continuous PWV measurements in larger and divers groups of people, potentially in collaboration with industry, NHS, and using sensor data from devices that people already own. Large data sets can then be analyzed together with other individual health related data by machine learning to identify ageing patterns and effect of healthcare interventions on trajectories of vascular ageing.

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