Linear affine transformations between 3-lead (Frank XYZ leads) vectorcardiogram and 12-lead electrocardiogram signals

BackgroundRecent advances in computer graphics and wireless technologies have renewed interest in vectorcardiogram (VCG) signals that use fewer leads than the conventional 12-lead electrocardiogram (ECG) signals for medical diagnostic applications. However, most cardiologists are accustomed to the 12-lead ECG even though some of the leads are either nearly aligned with or derived from the others and consequently contain redundant information. The ability to transform from orthogonal 3-lead VCG to 12-lead ECG enables the use of fewer leads for signal analysis, computer visualization, and wireless transmission of signals. This can also improve mobility, albeit limited, to the patients.Materials and MethodsWe present a statistical approach to transform 3-lead Frank VCG to 12-lead ECG signals and vice versa, based on Dower's pioneering work on lead tranformation. This approach enables compensation of baseline shifts and other constant biases present in long ECG data streams, so that the resulting statistical transforms can be more consistent and accurate. We compare the performance of the affine transform with that of Dower transform (from 3 to 12 and from 12 to 3) using the data from the PhysioNet PTB database.ResultsThe results show that for both myocardial infarction (MI) and healthy control (HC) subjects, the statistical affine transform presented here maps 3-lead VCG to12-lead ECG more accurately than Dower or other lead transformation matrices of the ECG recordings.DiscussionThis investigation also shows the limitations associated with single dipole assumption that underlies Dower's geometric transformation. The results also indicate that lead transformation accuracy can be improved using separate customized transforms to, for example, age or pathologic conditions (here, MI vs HC) than a single statistical or geometric transform. Pertinently, we find that the affine transform coefficients can serve as discriminating features for classification/discrimination of MI patients from HC subjects.