L0 norm based sparse regularization for non-invasive infarct detection using ECG signal

Non-invasive reconstruction of infarcts inside the heart from ECG signals is an important and difficult problem due to the need to solve a severely ill-posed inverse problem. To overcome this ill-posedness, various sparse regularization techniques have been proposed and evaluated for detecting epicardial and transmural infarcts. However, the performance of sparse methods in detecting non-transmural, especially endocardial infarcts, is not fully explored. In this paper, we first show that the detection of non-transmural endocardial infarcts poses severe difficulty to the prevalent algorithms. Subsequently, we propose a novel sparse regularization technique based on a variational approximation of L0 norm. In a set of simulation experiments considering transmural and endocardial infarcts, we compare the presented method with total variation minimization and L1 norm based regularization techniques. Experiment results demonstrated that the presented method outperformed prevalent algorithms by a large margin, particularly when infarction is entirely on the endocardium.