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Personalized models of human atrial electrophysiology derived from endocardial electrograms

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number7480861
Pages (from-to)735-742
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number4
Early online date30 May 2016
Accepted/In press2 May 2016
E-pub ahead of print30 May 2016
Published1 Apr 2017


King's Authors


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 s1 s2 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 protocol to characterize cellular properties and predict tissue electrophysiological function.

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