Our research aims to improve cardiovascular health assessment by analysing pulse wave signals such as blood pressure, blood flow, and photoplethysmograms (PPG). These signals can be measured in vivo using various devices, including wearable sensors. They are influenced by the heart, vasculature, respiratory system, and autonomic nervous system, making them a valuable source of information for evaluating human health.

We develop signal processing algorithms to assess cardiovascular, respiratory, and autonomic function, alongside biophysical models that simulate pulse waves under various physiological and pathological conditions. These models help reveal the mechanisms behind pulse wave behaviour and support the refinement of our algorithms. We apply these tools to clinically relevant problems, combining simulations and real-world data.

Our research publications are listed here.