title: Generalized training subset selection for statistical estimation of epicardial activation maps from intravenous catheter measurements.
abstract: Catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. This technique uses a linear 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 based on the spatial covariance of the measurement sites. We performed 14 dog experiments with various interventions to create an epicardial activation-time map database. This database included a total of 592 epicardial activation maps which were recorded using a sock array placed on the ventricles of dog hearts. We present five approaches, which examined sequential addition and removal of maps to select a generalized training set for the estimation technique. The selection consisted of choosing a subset of epicardial ectopic activation-time maps from the database of beats which resulted in estimation accuracy levels better than or at least similar to using all the maps in database. Our aim was to minimize the redundancy in the database and to be able to guide the eventual procedures required to obtain training data from open-chest surgery patients. The results from this study illustrated this redundancy and suggested that by including an optimal subset (around 100 maps) of the full database the estimation technique was able to perform as well as and even in some cases better than including all the maps in the database. The results also 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.
authors.affiliation: Biomedical Engineering Department, Başkent University, Ankara 06530, Turkey. byilmaz@baskent.edu.tr
authors.forename: Bülent
authors.initials: B
authors.lastname: Yilmaz
authors.forename: Robert S
authors.initials: RS
authors.lastname: MacLeod
mesh_terms: D000818:Animals; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D018780:Body Surface Potential Mapping / Q000706:statistics & numerical data*; D006328:Cardiac Catheterization; D002304:Cardiac Pacing, Artificial; D003196:Computer Graphics; D003936:Diagnosis, Computer-Assisted; D004285:Dogs; D004562:Electrocardiography / Q000706:statistics & numerical data*; D004566:Electrodes; D016014:Linear Models; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D010496:Pericardium / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D012984:Software