Aurore Lyon

Post doc

Dr Aurore Lyon graduated from Telecom ParisTech Engineering School in Paris (France) with a Master in Engineering, and from the University of Oxford (UK) with a Master in Computer Science. She obtained her PhD at the Department of Computer Science at the University of Oxford, working in the Computational Cardiovascular Science group. Her PhD focussed on using computational techniques for analysis, modelling and simulation of ECG signals for patient risk stratification in hypertrophic cardiomyopathy.

Since April 2018, she has been working at Maastricht University. Her current research focusses on cardiac electromechanics and the coupling from cellular electrophysiology to whole-heart mechanics and hemodynamics. Specific projects of research include electromechanical modelling, effect of exercise on cardiac pathologies such as ARVC or deformation imaging analysis in atrial fibrillation patients. Her interests lie in combining computer simulations with clinical data to better understand cardiac physiology (e.g. exercise) or disease mechanisms (e.g. arrhythmia).

 

Department of Biomedical Engineering
Universiteitssingel 50, 6229 ER Maastricht
PO Box 616, 6200 MD Maastricht
Room number: H3.348

  • 2020
    • Sutanto, H., Lyon, A., Lumens, J., Schotten, U., Dobrev, D., & Heijman, J. (2020). Cardiomyocyte calcium handling in health and disease: Insights from in vitro and in silico studies. Progress in Biophysics & Molecular Biology, 157, 54-75. https://doi.org/10.1016/j.pbiomolbio.2020.02.008
    • Lyon, A., Dupuis, L. J., Arts, T., Crijns, H. J. G. M., Prinzen, F. W., Delhaas, T., Heijman, J., & Lumens, J. (2020). Differentiating the effects of beta-adrenergic stimulation and stretch on calcium and force dynamics using a novel electromechanical cardiomyocyte model. American Journal of Physiology-heart and Circulatory Physiology, 319(3), H519-H530. https://doi.org/10.1152/ajpheart.00275.2020
    • van Osta, N., Lyon, A., Kirkels, F., Koopsen, T., van Loon, T., Cramer, M. J., Teske, A. J., Delhaas, T., Huberts, W., & Lumens, J. (2020). Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model. Philosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences, 378(2173), [20190347]. https://doi.org/10.1098/rsta.2019.0347
    • Hermans, B. J. M., Bennis, F. C., Vink, A. S., Koopsen, T., Lyon, A., Wilde, A. A. M., Nuyens, D., Robyns, T., Pison, L., Postema, P. G., & Delhaas, T. (2020). Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization. Heart Rhythm, 17(5), 752-758. https://doi.org/10.1016/j.hrthm.2019.12.020
  • 2019
    • Lyon, A., Mincholé, A., Bueno-Orovio, A., & Rodriguez, B. (2019). Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study. Morphologie, 103(343), 169-179. https://doi.org/10.1016/j.morpho.2019.09.001
    • Minchole, A., Camps, J., Lyon, A., & Rodriguez, B. (2019). Machine learning in the electrocardiogram. Journal of Electrocardiology, 57, S61-S64. https://doi.org/10.1016/j.jelectrocard.2019.08.008