Localized spatial discrimination of epicardial conduction paths after linear transformation of variant information.
We present a method for the localized statistical discrimination of class populations based on the Karhunen-Loève and Fukunaga-Koontz transforms. These transforms provide features that model the variance of a sample distribution. The spatial series of a 196 channel epicardial electrogram recording from an arrythmogenic postinfarct canine were analyzed. For each type of rhythm studied, Karhunen-Loève and Fukunaga-Koontz expansions were computed from five training sets of spatial data, corresponding to five locations across the surface of the heart. Nonparametric statistical tests were then used for discriminant analysis to compare properties representative of the distribution from each proposed class. In a comparison of properties from sinus rhythm to those of two ventricular tachycardias, several spatial regions exhibited statistically significantly different propagation characteristics. These areas were observed by visual inspection of electrogram activation maps to be characterized by conductive gradients, which differed in magnitude and direction from one rhythm to another. The regions in which the propagation characteristics are of greatest difference in each tachycardia were centered upon sites of conduction block, manifested by reentrant circuit rhythms. Therefore, the importance of the technique for the localization of specific electrophysiologic events is demonstrated. This study extends previous work of our group on biosignal pattern recognition to encompass localized spatial data.
Ciaccio
E J
EJ
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ.
Dunn
S M
SM
Akay
M
M
Wit
A L
AL
Coromilas
J
J
Costeas
C A
CA
D000704:Analysis of Variance; D000818:Animals; D016002:Discriminant Analysis; D004195:Disease Models, Animal; D004285:Dogs; D004562:Electrocardiography / Q000379:methods*; D005069:Evaluation Studies as Topic; D005163:Factor Analysis, Statistical; D006329:Heart Conduction System; D016014:Linear Models; D009203:Myocardial Infarction / Q000150:complications*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000209:etiology