Estimating the Minimal Size of Training Datasets Required for the Development of Linear ECG-Lead Transformations

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 12-lead ECG data of a testing dataset ( $n=186$ subjects) were used for the estimation of Frank VCGs. Root-mean-squared-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.