Reconstruction of cardiac activities from Vectorcardiography and Magnetocardiography using Bayesian approach with coherence mapping

The human heart needs electrical signals to contract and relax its muscles that helps in mechanical activities. Due to these electrical impulses, atiny electric and magnetic field are generated on and outside the thorax surface. These functional activities can be investigated using Electro/Magnetocardiogram (E/MCG). The challenging task in the research is to image the cardiac dysfunctions in 3D from E/MCG not at the surface level but to estimate the activities at the source level, called inverse problem. To solve this, one must model a generic structure of the heart enclosed in the thorax mesh and their spatial relation with location of the MCG detectors, called as forward problem. This paper proposes a novel approach in the construction of the spatial matrix based on vectors. The main objective of this paper is to solve MCG inverse problem using Bayesian approach with varying spatial matrix updates derived from Vectorcardiography signals. Also, we proposed to apply coherence estimation to check the connectivity between the reconstructed epicardial sources. The significance of the coherence map is to construct an outline of abnormality spread at the heart surface. The inverse problem is extended for disease cases with over, balanced and underdetermined conditions with noise/free environments.