The effect of interpolating low amplitude leads on the inverse reconstruction of cardiac electrical activity. (September 2021) - Rababah, Ali S.; Bear, Laura R.; Dogrusoz, Yesim Serinagaoglu; Good, Wilson; Bergquist, Jake; Stoks, Job; MacLeod, Rob; Rjoob, Khaled; Jennings, Michael; Mclaughlin, James; Finlay, Dewar D.
Abstract: Electrocardiographic imaging is an imaging modality that has been introduced recently to help in visualizing the electrical activity of the heart and consequently guide the ablation therapy for ventricular arrhythmias. One of the main challenges of this modality is that the electrocardiographic signals recorded at the torso surface are contaminated with noise from different sources. Low amplitude leads are more affected by noise due to their low peak-to-peak amplitude. In this paper, we have studied 6 datasets from two torso tank experiments (Bordeaux and Utah experiments) to investigate the impact of removing or interpolating these low amplitude leads on the inverse reconstruction of cardiac electrical activity. Body surface potential maps used were calculated by using the full set of recorded leads, removing 1, 6, 11, 16, or 21 low amplitude leads, or interpolating 1, 6, 11, 16, or 21 low amplitude leads using one of the three interpolation methods – Laplacian interpolation, hybrid interpolation, or the inverse-forward interpolation. The epicardial potential maps and activation time maps were computed from these body surface potential maps and compared with those recorded directly from the heart surface in the torso tank experiments. There was no significant change in the potential maps and activation time maps after the removal of up to 11 low amplitude leads. Laplacian interpolation and hybrid interpolation improved the inverse reconstruction in some datasets and worsened it in the rest. The inverse forward interpolation of low amplitude leads improved it in two out of 6 datasets and at least remained the same in the other datasets. It was noticed that after doing the inverse-forward interpolation, the selected lambda value was closer to the optimum lambda value that gives the inverse solution best correlated with the recorded one. Highlights: Removal of low amplitude signals of up to 11 leads did not change the reconstructed EGMs or the activation maps derived from them. Removal of low amplitude signals resulted in a better inverse solution as compared to the Laplacian interpolation and the hybrid interpolation. The Laplacian interpolation did not perform very well in interpolating low amplitude leads. IF interpolating the lowest amplitude signals of up to 11 electrodes improved the inverse solution in some datasets.