A personalized computational model demonstrated that re-entry occurs only when the dispersion of recovery is large (≥76 ms/cm) and the extra stimulus occurs just after local recovery ended (~ +40 ms).
Can a personalized computational framework using ECGI data simulate individual cardiac electrophysiology to study the interaction between electrical substrates and premature ventricular complexes?
A novel personalized computational framework using ECGI data enables the simulation of individual cardiac electrophysiology to better understand arrhythmia mechanisms and PVC interactions.
Electrocardiographic Imaging (ECGI) can unmask electrical abnormalities that were difficult to detect using the standard 12-lead ECG. However, it is still challenging to interpret the potential arrhythmogenic consequence of electrical patterns found with ECGI. Here, we introduce a computational framework that allows personalized simulations of cardiac electrophysiology (EP) to mimic electrical substrate as detected in an individual, to study the interaction between that substrate and premature ventricular complexes (PVCs).
Cluitmans et al. (Sun,) conducted a other in Ventricular fibrillation (n=1). Personalized computational electrophysiology modeling based on ECGI was evaluated on Inducibility of re-entry. A personalized computational model demonstrated that re-entry occurs only when the dispersion of recovery is large (≥76 ms/cm) and the extra stimulus occurs just after local recovery ended (~ +40 ms).