Volumetric Prediction of Cardiac Electrophysiology using a Heart Model Personalised to Surface Data

Abstract : Predictive cardiac electrophysiology models can provide a substantial aid in the success of the treatment of cardiac arrhythmias. Sufficiently accurate model predictions highly depend on the personalisation of the model i.e. estimation of patient-specific model parameters. In this paper, we evaluate the prediction ability of a simplified ionic 3D cardiac electrophysiology model, the Mitchell-Schaeffer model (2003), after personalisation. The personalisation is performed by optimising the model parameters, using the epicardial surface depolarisation and repolarisation maps obtained ex-vivo from optical imaging of large porcine healthy heart. We also evaluate the sensitivity of the personalisation method to a pacing location and the estimated parameter values. This is done by comparing the personalisation results obtained with left ventricle endocardial pacing location to those obtained with right ventricle epicardial pacing location. Later, using the personalised electrophysiology model, we predict the volumetric depolarisation and repolarisation time isochrones for right ventricle endocardial and left ventricle epicardial pacing locations and evaluate the epicardial isochrones against the actual maps obtained from the optical data.