Noninvasive Personalization of a Cardiac Electrophysiology Model from Body Surface Potential Mapping

Sophie Giffard-Roisin, Thomas Jackson, Lauren Fovargue, Jack Lee, Herve Delingette, Reza Razavi, Nicholas Ayache, Maxime Sermesant

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
108 Downloads (Pure)

Abstract

Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize 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 five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized 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 personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

Original languageEnglish
Article number7745951
Pages (from-to)2206-2218
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number9
Early online date29 Nov 2016
DOIs
Publication statusPublished - 1 Sept 2017

Keywords

  • Cardiac electrophysiology
  • ECG imaging
  • inverse problem of ECG
  • parameter estimation
  • personalization

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