Personalized Models of Human Atrial Electrophysiology Derived From Endocardial Electrograms

• Published in: IEEE transactions on biomedical engineering 2017-04, Vol.64 (4), p.735-742
• Main Authors: Corrado, Cesare, Whitaker, John, Chubb, Henry, Williams, Steven, Wright, Matthew, Gill, Jaswinder, O'Neill, Mark D., Niederer, Steven A.
• Format: Article
• Language: English
• Subjects: Algorithms; Atria; Atrial fibrillation (AF); Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Atrial Function; Atrium; Biological system modeling; Body Surface Potential Mapping - methods; catheter measurements; Catheters; computational model; Computational modeling; Computer applications; Computer Simulation; Conduction; conduction velocity; Customization; Diagnosis, Computer-Assisted - methods; Dimensional stability; effective refractory periods; Electric potential; Electrodes; electrograms; Electrophysiologic Techniques, Cardiac - methods; Electrophysiology; Endocardium - physiopathology; Errors; Fibrillation; Heart Conduction System - physiopathology; Humans; Mathematical models; Measurement methods; Medical instruments; model personalization; Models, Cardiovascular; Numerical models; Parameter estimation; Parameter identification; Parameterization; Properties (attributes); Protocols; Refractory period; Reproducibility of Results; restitution curves; Sensitivity and Specificity; Sheet modelling; Tissues; Two dimensional models; Velocity
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2016.2574619

Objective: 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. Methods: 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 s 1 -s 2 pacing 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%. Results: 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. Conclusion and significance: We have developed and demonstrated a robust and rapid approach for personalizing local ionic models from a clinically tractable