Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Abstract : Goal: We use non-invasive data (body surface potential mapping, BSPM) to personalise the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on 5 non-ischaemic implanted cardiac resynchonisation therapy (CRT) patients with a total of 21 different pacing conditions. Good personalisation results were found in terms of the activation onset location for the PVC (mean distance error, MDE=20.3mm), for the pacing sites (MDE=21.7mm) and for the CRT patients (MDE=24.6mm). We tested the predictive power of the personalised model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalised the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a non-invasive pre-operative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.
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Sophie Giffard-Roisin, Thomas Jackson, Lauren Fovargue, Jack Lee, Hervé Delingette, et al.. Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping. IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2017, IEEE Transactions on Biomedical Engineering, 64 (9), pp.2206 - 2218. ⟨10.1109/TBME.2016.2629849⟩. ⟨hal-01397393⟩

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