title: Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  We have personalized the cardiac EP model and predicted new patient-specific pacing conditions.
  
  
  
  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.
authors.forename: Sophie
authors.initials: S
authors.lastname: Giffard-Roisin
authors.forename: Thomas
authors.initials: T
authors.lastname: Jackson
authors.forename: Lauren
authors.initials: L
authors.lastname: Fovargue
authors.forename: Jack
authors.initials: J
authors.lastname: Lee
authors.forename: Herve
authors.initials: H
authors.lastname: Delingette
authors.forename: Reza
authors.initials: R
authors.lastname: Razavi
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.forename: Maxime
authors.initials: M
authors.lastname: Sermesant
mesh_terms: D000465:Algorithms; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D018802:Patient-Centered Care / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity