Numerical Investigation of Methods Used in Commercial Clinical Devices for Solving the ECGI Inverse Problem

Abstract

Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) combined with the patient specific anatomical data. In this work, we assess two ECGI algorithms used in commercial ECGI systems to solve the inverse problem; the Method of Fundamental Solutions (MFS) and the Equivalent Single Layer (ESL). We quantify the performance of these two methods in conjunction with two different activation maps to estimate the activation times and earliest activation sites. ESL provided more accurate reconstruction of the cardiac electrical activity, especially on the endocardial part of the heart. Nevertheless, both methods provided comparable results in terms of the derived activation maps and the localization of the focal origin as a clinically relevant parameter.