A Computational Investigation into the Effect of Infarction on Clinical Human Electrophysiology Biomarkers

L. Cardone-Noott, A. Bueno-Orovio, A. Mincholé, K. Burrage, M. Wallman, N. Zemzemi, E. Dall’Armellina, B. Rodriguez, In proceedings from CinC 2014 Cambridge, MA, USA, 7-10 September 2014.


The electrocardiogram (ECG) is often used to diagnose myocardial infarction, but sensitivity and specificity are low. Here we present a computational framework for solving the bidomain equations over an image-based human geometry and simulating the 12 lead ECG. First, we demonstrate this approach by evaluating a population of eight models with varying distributions of local action potential duration, and report that only the model with apicobasal and inter-ventricular heterogeneities produces concordant T waves. Second, we simulate the effects of an old anterior infarct, which causes a reduction in T wave amplitude and width. Our methodology can contribute to the understanding of ECG alterations under challenging conditions for clinical diagnosis.


LCN is funded by the EPSRC Systems Biology Doctoral Training Centre. AM holds a Marie Curie IntraEuropean Fellowship. ABO and BR are supported by BR’s Wellcome Trust Senior Research Fellowship in Basic Biomedical Sciences. This work used the ARCHER UK National Computing Service (http://www.archer.ac.uk).

Authors and Affiliations

  • Louie Cardone-Noott,  University of Oxford, Oxford, UK
  • Alfonso Bueno-Orovio, University of Oxford, Oxford, UK
  • Ana Mincholé, University of Oxford, Oxford, UK
  • Kevin Burrage, University of Oxford, Oxford, UK and Queensland University of Technology, Brisbane, Australia
  • Mikael Wallman, University of Oxford, Oxford, UK and Fraunhofer-Chalmers Centre, Gothenburg, Sweden
  • Nejib Zemzemi, INRIA Bordeaux Sud-Ouest, Bordeaux, France
  • Erica Dall’Armellina, University of Oxford, Oxford, UK and Oxford University Radcliffe Department of Medicine, Oxford, UK
  • Blanca Rodriguez, University of Oxford, Oxford, UK

Photo credits: Nic McPhee