An algorithm estimating heart translation and rotation reduced the root-mean-squared error of heart position from 13.7 mm to 0.1 mm in synthetic data and improved potential estimates in canine models.
An algorithm using electrocardiographic recordings on the heart and torso surfaces can accurately track heart position and reduce modeling errors in electrocardiographic imaging.
The accurate generation of forward models is an important element in general research in electrocardiography, and in particular for the techniques for ElectroCardioGraphic Imaging (ECGI). Recent research efforts have been devoted to the reliable and fast generation of forward models. However, these model can suffer from several sources of inaccuracy, which in turn can lead to considerable error in both the forward simulation of body surface potentials and even more so for ECGI solutions. In particular, the accurate localization of the heart within the torso is sensitive to movements due to respiration and changes in position of the subject, a problem that cannot be resolved with better imaging and segmentation alone. Here, we propose an algorithm to localize the position of the heart using electrocardiographic recordings on both the heart and torso surface over a sequence of cardiac cycles. We leverage the dependency of electrocardiographic forward models on the underlying geometry to parameterize the forward model with respect to the position (translation) and orientation of the heart, and then estimate these parameters from heart and body surface potentials in a numerical inverse problem. We show that this approach is capable of localizing the position of the heart in synthetic experiments and that it reduces the modeling error in the forward models and resulting inverse solutions in canine experiments. Our results show a consistent decrease in error of both simulated body surface potentials and inverse reconstructed heart surface potentials after re-localizing the heart based on our estimated geometric correction. These results suggest that this method is capable of improving electrocardiographic models used in research settings and suggest the basis for the extension of the model presented here to its application in a purely inverse setting, where the heart potentials are unknown.
Coll‐Font et al. (Mon,) conducted a other in Electrocardiographic Imaging (ECGI) forward model inaccuracy (n=3). Algorithm to estimate and correct heart translation and rotation vs. Nominal (uncorrected) forward model was evaluated on Relative error of inverse-computed heart potentials and body surface potentials. An algorithm estimating heart translation and rotation reduced the root-mean-squared error of heart position from 13.7 mm to 0.1 mm in synthetic data and improved potential estimates in canine models.