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Linear electrocardiographic lead transformations (LELTs) are used to estimate unrecorded ECG leads by applying a number of recorded leads to a LELT matrix. Such matrices are commonly developed using a training dataset. The size of the training dataset has an influence on the estimation performance of a LELT matrix. However, an estimate of the minimal size required for the development of LELTs has previously not been reported.The aim of this research was to determine such an estimate. We generated LELT matrices from differently sized (from n = 10 to n = 540 subjects in steps of 10 subjects) training datasets. The LELT matrices and the 12lead ECG data of a testing dataset (n = 186 subjects) were used for the estimation of Frank VCGs. Root-meansquared-error values between recorded and estimated Frank leads of the testing dataset were used for the quantification of the estimation performance associated with a given size of the training dataset.The performance of the LELTs was, after an initial phase of improvement, found to only marginally improve with additional increases in the size of the training dataset. Our findings suggest that the training dataset should have a minimal size of 170 subjects when developing LELTs that utilise the 12-lead ECG for the estimation of unrecorded ECG leads.

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Background:There are limited datasets available to facilitate the evaluation of patch-based lead systems, so the leads must be derived from existing data, mainly the 12-lead ECG. We have previously introduced a short spaced lead (SSL) system consisting of two leads with the largest ST segment changes during ischaemic-type episodes. In this study, we aim to evaluate the derivation of this patch-based lead system from the 12-lead ECG.Method: Thoracic body surface potential maps (BSPM) were recorded from n=734 patients. Using Laplacian interpolation, each recording was expanded to the 352-node Dalhousie torso. The eight independent channels of the 12lead ECG were extracted (I, II, V1-V6) with the two leads of the SSL patch Coefficients were derived using linear regression from the 12-lead ECG to the SSL patch. Results: The median Pearson correlation coefficients (CC) and root mean square error (RMSE) for each lead were calculated as follows (CC/RMSE): 0.986/74.3 µV (ST monitoring lead); 0.976/65.3 µV (spatially orthogonal lead). Conclusion:We have developed coefficients that allow the derivation of a patch-based lead system from the 12-lead ECG. Given the high correlation, it is possible to generate short spaced lead systems from existing diagnostic lead systems, however, amplitude errors are introduced in the process.