Realistic training data improve noninvasive reconstruction of heart-surface potentials.

The inverse problem of electrocardiography is to noninvasively reconstruct electrical heart activity from body-surface electrocardiograms. Solving this problem is beneficial to clinical practice. However, reconstructions cannot be obtained straightforwardly due to the ill-posed nature of this problem. Therefore, regularization schemes are necessary to arrive at realistic solutions. To date, no electrophysiological data have been used in reconstruction methods and regularization schemes. In this study, we used a training set of simulated heart-surface potentials to create a realistic basis for reconstructions of electrical cardiac activity. We tested this method in computer simulations and in one patient. The quality of reconstruction improved significantly after projection of the results of traditional regularization methods on this new basis, both in silico (p<0.01) and in vivo (p<0.05). Thus, we demonstrate that the novel concept of applying electrophysiological data might be useful to improve noninvasive reconstruction of electrical heart activity.