title: Reconstruction of multiple gastric electrical wave fronts using potential-based inverse methods.
abstract: One approach for non-invasively characterizing gastric electrical activity, commonly used in the field of electrocardiography, involves solving an inverse problem whereby electrical potentials on the stomach surface are directly reconstructed from dense potential measurements on the skin surface. To investigate this problem, an anatomically realistic torso model and an electrical stomach model were used to simulate potentials on stomach and skin surfaces arising from normal gastric electrical activity. The effectiveness of the Greensite-Tikhonov or the Tikhonov inverse methods were compared under the presence of 10% Gaussian noise with either 84 or 204 body surface electrodes. The stability and accuracy of the Greensite-Tikhonov method were further investigated by introducing varying levels of Gaussian signal noise or by increasing or decreasing the size of the stomach by 10%. Results showed that the reconstructed solutions were able to represent the presence of propagating multiple wave fronts and the Greensite-Tikhonov method with 204 electrodes performed best (correlation coefficients of activation time: 90%; pacemaker localization error: 3 cm). The Greensite-Tikhonov method was stable with Gaussian noise levels up to 20% and 10% change in stomach size. The use of 204 rather than 84 body surface electrodes improved the performance; however, for all investigated cases, the Greensite-Tikhonov method outperformed the Tikhonov method.
authors: Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand. juliana.kim@auckland.ac.nz
authors: J H K
authors: JH
authors: Kim
authors: A J
authors: AJ
authors: Pullan
authors: L K
authors: LK
authors: Cheng
mesh_terms: D000465:Algorithms; D001830:Body Surface Area; D004560:Electricity; D004562:Electrocardiography / Q000379:methods*; D004566:Electrodes; D006801:Humans; D008954:Models, Biological; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013270:Stomach