TV Regularized Low-Rank Framework for Localizing Premature Ventricular Contraction Origin

Abstract

Premature ventricular contraction (PVC) can cause great harm to human health. Both invasive and non-invasive techniques for detecting electrical activity of PVC or locating ectopic pacemakers are used in clinical diagnosis. Among them, the electrocardiographic imaging is a popular method for non-invasive reconstruction of cardiac electrophysiology through body-surface potential (BSP). In this paper, we propose a novel framework based on low-rank and sparse decomposition (LSD) + total variation (TV) to solve the ill-posedness of the spatiotemporal ECG-inverse problem to reconstruct the cardiac electrical activity of PVC. The proposed framework considers the spatiotemporal distribution of multi-frame cardiac potential as a whole. The TV is used to filter out relatively smooth candidates from countless inverse solutions. In addition, LSD utilizes the low-rank characteristic of the potential background and the sparseness of the potential outliers to avoid the loss of potential details and improve the accuracy of potential reconstruction. This improves the quality of electrical activity retrieval and the accuracy of locating the PVC origin. The simulation experiments of ventricular pacing reconstruction and diagnostic experiments of real PVC patients prove that the proposed framework is superior to the conventional quadratic methods (Tikhonov-0 and Tikhonov-2) and the non-quadratic method TV.

Keywords

• Inverse problem of electrocardiography
• total variation
• low-rank
• sparsity