A computational investigation into the effect of infarction on clinical human electrophysiology biomarkers

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.