A considerable number of sudden unexpected cardiac deaths occur every year in developed countries. Non-invasive techniques to identify patients at risk, provide accurate diagnosis and ablation guidance therapy currently under study. One of them is the electrocardiographic imaging (ECGI), which is a non-invasive imaging modality used to reconstruct cardiac electrophysiological data on the heart, and to map cardiac electrical excitation in relation to the heart‟s anatomy. Solution of the ECGI inverse ...
A considerable number of sudden unexpected cardiac deaths occur every year in developed countries. Non-invasive techniques to identify patients at risk, provide accurate diagnosis and ablation guidance therapy currently under study. One of them is the electrocardiographic imaging (ECGI), which is a non-invasive imaging modality used to reconstruct cardiac electrophysiological data on the heart, and to map cardiac electrical excitation in relation to the heart‟s anatomy. Solution of the ECGI inverse problem (or signal reconstruction) depends on specification of the relationship between potential sources on the cardiac surface and the body surface measured potentials (the forward problem). Despite all the success of the ECGI technique in the last years, the understanding and treatment of many cardiac diseases is not feasible yet without an improvement of the inverse problem solution. In this work, we first compare two configurations of the method of fundamental solutions (MFS), a meshless forward problem. Afterwards, we transfer and adapt four inverse problem methods to the ECGI setting: algebraic reconstruction technique (ART), random ART, ART Split Bregman (ART-SB) and range restricted generalized minimal residual (RRGMRES) method. We test all these methods with data from the Experimental Data and Geometric Analysis Repository (EDGAR) and compare their solution with the reference heart recorded potentials provided by EDGAR and a generalized minimal residual (GMRES) iterative method computed solution. Isochrone activation maps are also computed and compared. The results show that ART reaches the most stable solution and, in many cases, returns the best reconstruction. Differences between ART and random ART are almost negligible, and the accuracy of their solution is followed by RRGMRES, ART-SB and finally the GMRES (which performs the worst reconstructions). The RRGMRES method provides the best reconstruction in terms of morphology in some case, but it results to be less stable than ART when comparing different datasets. To conclude, we show that ART, random ART and RRGMRES proposed methods improve the GMRES, which was the best suggested inverse problem method when using MFS.
+
