Instance Selection for Estimation of Epicardial Activation Sequence from Venous Catheter Measurements

Catheter-based electrophysiological studies of the outer surface of the heart (epicardium) are limited to regions near the heart vessels or require transthoracic access. We have developed a statistical signal processing approach by which to estimate high-resolution epicardial activation maps from multi-electrode venous catheter measurements. This technique uses a linear minimum mean-squared Bayesian estimation model that derives a relationship between venous catheter measurements and unmeasured epicardial sites from a set of previously recorded, high-resolution epicardial activation-time maps used as a training data set. The training data set selection consisted of choosing a subset of epicardial activation-time maps from a database that could be used in all possible test cases with focal ectopic activity. In this study, our hypothesis was that the number of maps necessary for successful estimation could be reduced without a significant loss of performance. We developed three approaches for this purpose. Our results showed that 100 maps would be sufficient to obtain an estimation accuracy level that was better than all 470 maps paced from all over the epicardium. The results suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting and with fewer maps we can obtain similar reconstruction accuracy levels.