Abstract
The conduction properties of the atrioventricular (AV) node have a significant impact on heart rate during permanent atrial fibrillation (AF), and can be modulated through the use of β-blockers or calcium channel blockers. These drugs have different physiological effects and are often selected empirically. Hence, an improved understanding of how these drugs affect the AV node conduction properties may contribute to personalized treatment of AF. We propose a novel methodology for estimating the refractory period and conduction delay dynamics of the fast and slow pathways of the AV node from 24-hour ambulatory ECG recordings. Our approach comprises a network model of the AV node, a problem-specific genetic algorithm, and an approximate Bayesian computation algorithm for estimating the posterior distribution of the AV node properties. We analyzed 24-hour ambulatory ECG recordings at baseline from 51 patients with permanent AF. Interestingly, a moderate correlation between the short-term variability in the refractory period for the fast pathway and reduction in heart rate during treatment with metoprolol (ρ = 0.48, p < 0.005) was found. Thus, the proposed methodology enables individualized characterization of the AV node and can potentially assist in treatment selection.