Rina Ariga

My research interest is in integrating high-dimensional data from imaging, electrocardiograms (ECG), genetics, blood analysis and health records of patients with cardiovascular disease to gain a better understanding of the underlying pathophysiology. My work is currently focused on the most common inherited heart disease – Hypertrophic Cardiomyopathy (HCM), which is an important cause of sudden cardiac death in young people. 

My doctoral research pioneered a novel imaging method for HCM patients to measure heart muscle disarray  – a microstructural abnormality previously only seen at post mortem, which is believed to be the focus for fatal heart rhythms. I have received several prizes and awards for this work, and the findings were reported in the media.

I have also demonstrated, in collaboration with Oxford’s Computational Cardiovascular Science Team, a machine learning approach to the ECG to detect subtle electrical changes indicative of such structural defects, and the risk of abnormal rhythms. We used computational modelling and simulation of the heart based on magnetic resonance imaging to reveal distinct mechanisms for specific electrical patterns, which may pave the way towards individualised patient treatment.

My ongoing work is in the development of disarray imaging for use in a multicentre prospective study and the application of machine learning in ECG and multiparametric cardiac magnetic resonance imaging.