A Multi-Disciplinary Approach to Solve the Inverse Problem of Electro- and Magnetocardiography

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In electrocardiography (ECG), the heart is studied by interpreting voltages measured on the body surface. The spatial accuracy of the ECG can be improved by adding more sensors and by modeling the signals with the help of physics and mathematics. In the inverse problem of ECG, the aim is to estimate the sources of the cardiac electric sources or the electric potential on the surface of the heart from the measured signal at the body surface. This problem is illposed: simplifying assumptions, a-priori information, and regularization are needed.The aim of this project is to increase the value of ECG as a tool for cardiac diagnosis by developing methods for solving the inverse problem of electrocardiography.The first research lead will concentrate on an increase in the reconstruction quality using model-based spatio-temporal regularization techniques. The second approach aims to assess the capability of electro- and magnetocardiographic imaging of reconstructing clinically relevant information in the presence of incomplete models and data. In a third lead, the focus will be on new mathematical approaches like making the solver faster and more effective, which can be achieved by a decomposition of the large-scale ill-conditioned problem. Another aspect is the use of different error norms and regularization schemes.