Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation.
A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, 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. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.
Yilmaz
Bülent
B
Biomedical Engineering Department of Başkent University, Ankara, Turkey. byilmaz@baskent.edu.tr
MacLeod
Robert S
RS
Punske
Bonnie Billard
BB
Taccardi
Bruno
B
Brooks
Dana H
DH
D000465:Algorithms; D001185:Artificial Intelligence; D018780:Body Surface Potential Mapping / Q000379:methods*; D005117:Cardiac Complexes, Premature / Q000175:diagnosis / Q000503:physiopathology*; D002405:Catheterization, Central Venous / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006329:Heart Conduction System / Q000503:physiopathology*; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010496:Pericardium / Q000503:physiopathology*