Dr Sau is a clinical research fellow and cardiology registrar currently undertaking a PhD under the supervision of Dr Fu Siong Ng, Prof Nicholas S Peters and Prof Danilo Mandic.
Dr Sau’s main research interest is the application of machine learning to further the field of cardiac electrophysiology, including applying deep learning to the surface ECG and to intracardiac electrograms.
He studied medicine at Imperial College London, where he was awarded a First Class (Hon) BSc degree in Medical Sciences with Cardiovascular sciences and Distinctions in Medical Sciences, Clinical Science and Clinical Practice. He has been awarded a Postgraduate Certificate in Medical Education by the University of Dundee.
His postgraduate clinical training to date has been in the North West Thames deanery, most recently as an NIHR Academic Clinical Fellow. During his ST4 year he was awarded a British Heart Foundation Clinical Research Training Fellowship and started this in October 2021.
Dr Sau maintains a strong interest in clinical electrophysiology and is an aspiring cardiac electrophysiologist.
Sau A, Ng FS, 2023, Hypertrophic cardiomyopathy risk stratification based on clinical or dynamic electrophysiological features: two sides of the same coin., Europace
Sau A, 2023, Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia, Cardiovascular Digital Health Journal, ISSN:2666-6936, Pages:1-8
et al., 2022, A fully-automated paper ECG digitisation algorithm using deep learning, Scientific Reports, Vol:12, ISSN:2045-2322
et al., 2022, Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms, European Heart Journal – Digital Health, Vol:3, ISSN:2634-3916, Pages:405-414
et al., 2022, Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps, Plos One, Vol:17, ISSN:1932-6203, Pages:1-24