Spatiotemporal characterization of paced cardiac activation with body surface potential mapping and self-organizing maps.
In this study self-organizing maps (SOM) were utilized for spatiotemporal analysis and classification of body surface potential mapping (BSPM) data. Altogether 86 cardiac depolarization (QRS) sequences paced by a catheter in 18 patients were included. Spatial BSPM distributions at every 5 ms over the QRS complex were first presented to an untrained SOM. The learning process of the SOM units organized the maps in such a way that similar BSPMs are represented in particular areas of the SOM network. Thereafter, time trajectories and distance maps were created on the trained SOM from sequential maps in a selected paced QRS. The trajectories and distance maps can be applied as such for the localization of abnormal ventricular activation, as well as quantitative input for statistical classification. The results indicate that the method has potential for locating endocardial sites of abnormal ventricular activation, despite the patient material being too limited to provide a reliable statistical evaluation of the source localization accuracy.
D000465:Algorithms; D001830:Body Surface Area; D004566:Electrodes; D006801:Humans; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D010138:Pacemaker, Artificial; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000503:physiopathology*