Estimating ECG distributions from small numbers of leads

The utility of body surface potential mapping to improve interpretation of electrocardiographic information lies in the presentation of thoracic surface distributions to characterize underlying electrophysiology less ambiguously than that afforded by conventional electrocardiography. Localized cardiac disease or abnormal electrophysiology presents itself electrocardiographically on the body surface in a manner in which pattern plays an important role for identifying or characterizing these abnormalities. Thus, in myocardial infarction, transient myocardial ischemia, Wolff-Parkinson-White syndrome, or ventricular ectopy, observation of elecrocardiographic potential patterns, their extrema, and their magnitudes permits localization and quantization of the abnormal activity. Conventional electrocardiography assesses pattern information incompletely and does not use information of distribution extrema locations or magnitudes. Thus, increases or decreases in the magnitudes of electrocardiographic features (ST-segment potential displacement, amplitude, or morphology of Q, R, S, or T waves) associated with changes in cardiac sources (ischemia, infarction, conduction abnormalities, etc.) as measured from fixed leads have a high likelihood of being misinterpreted if the distribution itself is changing. In this study, the authors demonstrate the utility of estimating distributions from small numbers of optimally selected leads, including conventional leads, to reduce uncertainty in the interpretation of electrocardiographic information. This issue is highly relevant when thresholds are used to detect significance of potential levels (exercise testing, detection of myocardial infarction, and continuous monitoring to assess ST-segment changes). Significance of this work lies in improved detection and characterization of abnormal electrophysiology using conventional or enhanced leadsets and methods to estimate thoracic potential distributions.