Statistical and deterministic approaches to designing transformations of electrocardiographic leads.
Two different approaches can be used to investigate the relationships among electrocardiographic leads: a statistical one, based on the analysis of recorded electrocardiograms (ECGs), and a deterministic one, based on physical principles that govern the current flow in irregularly shaped volume conductors such as the human body. The purpose of this study was to compare these two approaches. For the statistical investigation, the data set consisted of 120-lead ECGs recorded in a population including normal subjects (n = 290), post-myocardial-infarction patients (n = 497), patients with a history of ventricular tachycardia but no evidence of a previous myocardial infarction (n = 105), and patients with a single-vessel coronary artery disease who underwent coronary angioplasty (n = 91). Lead transformations of interest were obtained by fitting the multiple-regression model to this data set by the least-squares method. For the deterministic investigation, we used a boundary-element model of the human torso to simulate body-surface potentials in response to three orthogonal unit dipoles placed consecutively at 1,239 ventricular source locations, and the resulting body-surface potential distributions (instead of the recorded ECGs) were then fitted by the multiple-regression model. The results suggest that the lead transformations should be preferably designed by statistical analysis of recorded ECGs. Regression models with a small number of predictors (eg, those based on three ECG leads) are the most reliable; those using more predictors are fraught with the danger of collinearity when predictors are highly correlated (as occurs in the standard 12-lead ECG). Model-derived deterministic transformations are compatible with statistically derived ones, provided that the distributed character of the cardiac sources is taken into account. We conclude that statistical associations among electrocardiographic leads can be reliably quantified in sufficiently large and diverse databases of recorded data; the causality of these associations can be supported by appropriate deterministic models based on the laws of physics.
Horácek
B Milan
BM
Faculty of Medicine of Dalhousie University, Halifax, Nova Scotia, Canada. milan.horacek@dal.ca
Warren
James W
JW
Feild
Dirk Q
DQ
Feldman
Charles L
CL
D015906:Angioplasty, Balloon, Coronary; D018780:Body Surface Potential Mapping; D004562:Electrocardiography / Q000379:methods*; D004566:Electrodes; D006801:Humans; D008953:Models, Anatomic; D009203:Myocardial Infarction / Q000503:physiopathology; D012044:Regression Analysis; D017180:Tachycardia, Ventricular / Q000503:physiopathology