Performance of generalized eigensystem and truncated singular value decomposition methods for the inverse problem of electrocardiography

摘要

Singular Value Decomposition (SVD) and Generalized Eigensystem (GES) inverse techniques are compared for their ability to solve the inverse problem of electrocardiography. In the inverse problem of electrocardiography, electrical potential data for numerous locations on the body (torso) surface is used to infer the electrical potentials on the heart surface, while the governing equation and material properties are assumed known. This paper addresses two areas. First, the previously observed improved performance of GES compared to SVD is explained in terms of the unique nature of the GES vectors. Second, epicardial data from sixin-vitrorabbit heart experiments are used to project body surface data for six different geometries, and inverse solutions are computed both with and without added noise. For concentric geometries, GES outperformed SVD in all instances. For eccentric heart/body geometries, GES outperformed SVD when the inverse errors themselves were small. In all cases, GES was less sensitive to added noise than SVD.