Jordi Alastruey, Ph.D.
School of Biomedical Engineering and Imaging Sciences, King’s College London
Jordi Alastruey received the M.Sc. degree in civil engineering from the Universitat Politècnica de Catalunya, Barcelona, Spain, in 2002, and the Ph.D. degree in biomedical engineering from Imperial College London (ICL), London, U.K., in 2006. In 2009 he was awarded a British Heart Foundation Research Fellowship; began his fellowship at ICL before moving to King’s College London in 2011, where he is currently a Senior Lecturer with the School of Biomedical
Engineering and Imaging Sciences. He leads the Haemodynamic Modelling Research Group which specialises in the assessment of cardiovascular function based on the analysis of pulse wave (PW) signals. These signals are influenced by the heart, vasculature, and respiratory and autonomic nervous systems, making them a rich source of information to assess human health.
He and his colleagues develop novel models for simulating PW signals, such as blood pressure and photoplethysmogram waves, under a variety of physiological and pathophysiological
conditions. They develop methods for calibrating these models and understanding the physical mechanisms underlying their results. They also investigate signal processing techniques to assess the functions of the cardiovascular, respiratory, and autonomic nervous systems from PW signals acquired by a variety of devices, including wearable sensors. The seminar will focus on the simulation of PW signals using biophysical modelling and on the creation of
datasets of synthetic PWs representative of samples of real subjects. These datasets are a cost-effective approach for the development and pre-clinical testing of technologies to assess cardiovascular function under a wide range of physiological conditions. They also allow us to understand biophysical mechanisms underlying correlations observed from populations of real subjects and train machine-learning algorithms for PW analysis. The seminar will present the benefits of using synthetic PW datasets in the assessment of vascular ageing and arterial hypertension from PW signals.