Stefan van Duijvenboden

Stefan is a researcher in the wearables group at the Big Data Institute and Oxford Population Health.

He develops methods to analyses complex time-series datasets to investigate if wearable electrocardiogram (ECG) sensors can improve the prediction of, and discovery of novel mechanisms for, cardiovascular disease. 

One of Stefan's main research interests is the interaction between the autonomic nervous system and the heart. An increasing amount of evidence suggests that the autonomic nervous system is crucial in the development and progression of cardiovascular disease.

He has conducted several studies to infer nervous control of the heart, ranging from experimental studies measuring intracardiac ECGs in patients in a catheterisation lab to large population studies in UK Biobank to assess the genetic architecture and prognostic value of (exercise) ECG markers. 

Stefan was originally trained as a technical physician (MSc, University of Twente, Netherlands) which allowed him to combine knowledge of (patho) physiology with skills in acquisition, processing and interpretation of biomedical signals to improve diagnostics and prognostics in healthcare.

During his PhD studies in biomedical engineering at UCL, Stefan performed experimental studies in patients to investigate how cardiac repolarisation is controlled by the autonomic nervous system.

After that, he joined the Electrogenomics Group at UCL and Queen Mary University of London to investigate the genetic influences in the response of the cardiac electrical system to exercise. The work involved processing large volume ECG data and conducting genetic association and survival analyses of the derived ECG markers.