title: Personalized Models of Human Atrial Electrophysiology Derived From Endocardial Electrograms.
abstract: Computational models represent a novel framework for understanding the mechanisms behind atrial fibrillation (AF) and offer a pathway for personalizing and optimizing treatment. The characterization of local electrophysiological properties across the atria during procedures remains a challenge. The aim of this work is to characterize the regional properties of the human atrium from multielectrode catheter measurements.
  
  
  
  We propose a novel method that characterizes regional electrophysiology properties by fitting parameters of an ionic model to conduction velocity and effective refractory period restitution curves obtained by a s1-s2pacing protocol applied through a multielectrode catheter. Using an in-silico dataset we demonstrate that the fitting method can constrain parameters with a mean error of 21.9 ± 16.1% and can replicate conduction velocity and effective refractory curves not used in the original fitting with a relative error of 4.4 ± 6.9%.
  
  
  
  We demonstrate this parameter estimation approach on five clinical datasets recorded from AF patients. Recordings and parametrization took approx. 5 and 6 min, respectively. Models fitted restitution curves with an error of ~ 5% and identify a unique parameter set. Tissue properties were predicted using a two-dimensional atrial tissue sheet model. Spiral wave stability in each case was predicted using tissue simulations, identifying distinct stable (2/5), meandering and breaking up (2/5), and unstable self-terminating (1/5) spiral tip patterns for different cases.
  
  
  
  We have developed and demonstrated a robust and rapid approach for personalizing local ionic models from a clinically tractable.
authors.affiliation: Department of Biomedical Engineering, King's College London, London, U.K.
authors.forename: Cesare
authors.initials: C
authors.lastname: Corrado
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: John
authors.initials: J
authors.lastname: Whitaker
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Henry
authors.initials: H
authors.lastname: Chubb
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Steven
authors.initials: S
authors.lastname: Williams
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Matthew
authors.initials: M
authors.lastname: Wright
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Jaswinder
authors.initials: J
authors.lastname: Gill
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Mark D
authors.initials: MD
authors.lastname: ONeill
authors.affiliation: Department of Biomedical EngineeringKing's College London.
authors.forename: Steven A
authors.initials: SA
authors.lastname: Niederer
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis / Q000503:physiopathology; D016275:Atrial Function; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D022062:Electrophysiologic Techniques, Cardiac / Q000379:methods*; D004699:Endocardium / Q000503:physiopathology; D006329:Heart Conduction System / Q000503:physiopathology*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity