Why the study?
Although mechanistic models of atrial electrophysiology have become increasingly realistic, their growing complexity makes estimating electrophysiological parameters to identify arrhythmogenic substrates in AF challenging.
A novel simplified mechanistic model for multi-electrode epicardial electrograms effectively estimates atrial cell conductivities and improves abnormality detection in AF tissue models.
May aid preclinical AF modeling; leaves open human validation before clinical use.
Atrial fibrillation (AF) is a complex, multifactorial heart disorder with mechanisms that are not yet fully understood. Numerical methods for estimating the conductivities of atrial cells are crucial for identifying the arrhythmogenic substrate responsible for AF. Although mechanistic models of atrial electrophysiology have become increasingly realistic, their complexity has grown over the years, making it challenging to estimate electrophysiological parameters from these models. In this work, we first introduce a simplified mechanistic model for multi-electrode epicardial electrograms, which balances the complexity of cardiac electrophysiology in AF with model simplicity. Based on this model, we derived a cross-power spectrum model capable of capturing time-spatial-frequency information from multi-electrode data for estimating conductivity parameters. As the parameter estimation problem is highly ill-posed and can lead to unstable solutions, we integrated simultaneous confirmatory factor analysis with an improved regularization strategy to robustly estimate conductivity parameters. The experimental results on different types of tissue demonstrate the effectiveness of our model and method in estimating conductivities in AF and demonstrate the improvement of abnormality detection in the tissue.
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Sun et al. (2025) studied this question.
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