Non-Invasive Characterization of Atrio-Ventricular Properties During Atrial Fibrillation

Abstract

The atrio-ventricular (AV) node is the primary regulator of ventricular rhythm during atrial fibrillation (AF). Hence, ECG based characterization of AV node properties can be an important tool for monitoring and predicting the effect of rate control drugs. In this work we present a network model of the AV node, and an associated workflow for robust estimation of the model parameters from ECG. The model consists of interacting nodes with refractory periods and conduction delays determined by the stimulation history of each node. The nodes are organized in one fast pathway (FP) and one slow pathway (SP), interconnected at their last nodes. Model parameters are estimated using a genetic algorithm with a fitness function based on the Poincare plot of the RR interval series. The robustness of the parameter estimates was evaluated using simulated data based on ECG measurements. Results from this show that refractory period parameters <tex>$R_{min}^{SP}$</tex> and <tex>$\Delta R^{SP}$</tex> can be estimated with an error <tex>$(mean\pm std)$</tex> of <tex>$10\pm 22\ ms\ and-12.6\pm 26\ ms$</tex> respectively, and conduction delay parameters <tex>$D_{min,tot}^{SP}$</tex> and <tex>$\Delta D_{tot}^{SP}$</tex> with an error of <tex>$7\pm 35\ ms$</tex> and <tex>$4\pm 36\ ms$</tex>. Corresponding results for the fast pathway are <tex>$31.7\pm 65\ ms, -0.3\pm 77\ ms$</tex>, and 1 <tex>$7\pm 29\ ms,43\pm 109\ ms$</tex>. This suggest that AV node properties can be assessed from ECG during AF with enough precision and robustness for monitoring the effect of rate control drugs.