title: Identification of nonlinear cardiac cell dynamics using radial basis function regression.
abstract: We present a novel method for the identification of the dynamics of physiological cardiac cell models. The main aim of the technique is to improve the computational efficiency of large-scale simulations of the electrical activity of the heart. The method identifies the dynamical attractor of a detailed physiological model using statistical learning techniques. In particular, a radial basis function regression method is used to capture the intrinsic dynamical features of the model, thus reducing the computational cost to quantitatively generate cardiac action potentials in a wide range of pacing conditions. The approach permits to recover key properties such as the action potential morphology and duration in a wide range of pacing frequencies.
authors.affiliation: Department of Software, Universitat Politecnica de Catalunya, Comte Urgell 187, 08036 Barcelona, Spain. samir.kanaan@upc.edu
authors.forename: Samir
authors.initials: S
authors.lastname: Kanaan-Izquierdo
authors.forename: Susana
authors.initials: S
authors.lastname: Velazquez
authors.forename: Raul
authors.initials: R
authors.lastname: Benitez
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000503:physiopathology; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods; D006321:Heart / Q000502:physiology; D006352:Heart Ventricles / Q000473:pathology; D006801:Humans; D015233:Models, Statistical; D009206:Myocardium / Q000166:cytology* / Q000473:pathology; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D015203:Reproducibility of Results