L0 Norm Based Sparse Regularization for Noninvasive Infarct Detection Using ECG Signal

Publication
In Computing in Cardiology (CinC), 2017

Summary

Several sparse regularization techniques have been proposed and evaluated for detecting epicardial and transmural infarcts. But their performance on non-transmural, especially endocardial infarcts, is not fully explored. Here, we first show that the detection of non-transmural endocardial infarcts presents severe difficulty to the prevalent sparse algorithms like L1 norm or total variation regularization. Subsequently, we propose a sparse regularization technique based on a variational approximation of L0 norm- by using Legendre-Fenchel duality of convex functions. We demonstrate that the presented method outperforms existing algorithms by a descent margin, particularly when infarction is entirely on the endocardium.

Sandesh Ghimire
Postdoctoral Researcher

My research interests include machine learning, computer vision and medical imaging.