Dr Stefan van Duijvenboden
Stefan van Duijvenboden
Researcher in Health Data Science
I am a researcher in the wearables group at the Big Data Institute and Nuffield Department of Population Health. I develop methods to analyse 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 my 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. I have 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.
I was originally trained as a technical physician (MSc, University of Twente, Netherlands) which allowed me to combine knowledge of (patho)physiology with skills in acquisition, processing and interpretation of biomedical signals to improve diagnostics and prognostics in healthcare. During my PhD studies in biomedical engineering at UCL, I performed experimental studies in patients to investigate how cardiac repolarisation is controlled by the autonomic nervous system.
After my PhD studies, I 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.
Prognostic Significance of Different Ventricular Ectopic Burdens During Submaximal Exercise in Asymptomatic UK Biobank Subjects.
van Duijvenboden S. et al, (2023), Circulation
Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning.
Naderi H. et al, (2023), Eur Heart J Digit Health, 4, 316 - 324
Genome-Wide Analysis of Left Ventricular Maximum Wall Thickness in the UK Biobank Cohort Reveals a Shared Genetic Background With Hypertrophic Cardiomyopathy.
Aung N. et al, (2023), Circ Genom Precis Med
Prediction of Coronary Artery Disease and Major Adverse Cardiovascular Events Using Clinical and Genetic Risk Scores for Cardiovascular Risk Factors.
Ramírez J. et al, (2022), Circulation. Genomic and precision medicine, 15
ECG T-Wave Morphologic Variations Predict Ventricular Arrhythmic Risk in Low- and Moderate-Risk Populations.
Ramírez J. et al, (2022), Journal of the American Heart Association, 11