Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations

Abstract

Electrocardiographic Imaging (ECGI) requires robust ECG forward simulations to accurately calculate cardiac activity. However, many questions remain regarding ECG forward simulations, for instance: there are not common guidelines for the required cardiac source sampling. In this study we test equivalent double layer (EDL) forward simulations with differing cardiac source resolutions and different spatial interpolation techniques. The goal is to reduce error caused by undersampling of cardiac sources and provide guidelines to reduce said source undersampling in ECG forward simulations. Using a simulated dataset sampled at 5 spatial resolutions, we computed body surface potentials using an EDL forward simulation pipeline. We tested two spatial interpolation methods to reduce error due to undersampling triangle weighting and triangle splitting. This forward modeling pipeline showed high frequency artifacts in the predicted ECG time signals when the cardiac source resolution was too low. These low resolutions could also cause shifts in extrema location on the body surface maps. However, these errors in predicted potentials can be mitigated by using a spatial interpolation method. Using spatial interpolation can reduce the number of nodes required for accurate body surface potentials from 9,218 to 2,306. Spatial interpolation in this forward model could also help improve accuracy and reduce computational cost in subsequent ECGI applications.

Incorporating structural abnormalities in equivalent dipole layer based ECG simulations

“…The surfaces of all anatomical structures are discretized as closed triangulated surface meshes. A( y, x) is computed using the boundary element method from each triangle of the discretized ventricular surface towards each observation point at or within the torso (Oostendorp and van Oosterom, 1991;Tate et al, 2019) The electric activity at the myocardial surface at position x follows the local transmembrane potential waveform (van Oosterom, 2002;van Oosterom, 2003). At each node, the source strength is proportional to the local transmembrane potentials.…”

“…To reflect the propagation of depolarization over each discretized triangle elements at the surface of the myocardium, the source strength at time t of a triangle partially activated is weighted with the fraction of the triangle, that is, activated at t, as previously described (Tate et al, 2019).…”

A Unified Pipeline for ECG Imaging Testing

“…The Consortium for ECG Imaging (CEI) [1] was formed to leverage collaborative efforts toward overcoming technical challenges associated with ECGI. By focusing the efforts of multiple research groups around the world, the CEI has formed collaborative projects to evaluate and improve many of the technical aspects of ECGI, including: generating forward models [2][3][4][5][6], pre-processing body surface recordings [7][8][9], inverse calculation method [2,10], activation and recovery time determination, and others. Each of these groups has made progress to improve ECGI, yet these efforts are still part of many disparate software pipelines, many of which are not available.…”