Comparison of Kalman filter and Bayesian-MAP approaches in the spatio-temporal solution of the inverse electrocardiography

Abstract

In this study some of the spatial and spatio-temporal methods for the solution of the inverse problem of electrocardiography (ECG) are compared with each other. Comparisons are also made for the cases with geometric errors, where the location of the heart is shifted for 10mm and the size of the heart is reduced by 5%. The compared methods are the Kalman filter and Bayesian maximum a posteriori estimation (MAP). Two different Bayesian-MAP algorithms are used. While one uses only spatial information the other uses spatio-temporal information. In Kalman filter algorithms the state transition matrix (STM) that contains the spatio-temporal information is calculated with two different scenarios. In the first case the STM is calculated directly from the training set and in the second case the solution of the spatial Bayesian-MAP is employed to calculate STM.