Application of L0-norm regularization to epicardial potential reconstruction

Liansheng Wang, Xinyue Li, Yiping Chen, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Inverse problem of electrocardiography (ECG) has been extensively investigated as the estimated epicardial potentials (EPs) reflecting underlying myocardial activities. Traditionally, L2-norm regularization methods have been proposed to solve this ill-posed problem. But L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using L1-norm penalty function, however, may greatly increase the computational complexity due to its non-differentiability. In this study, we present a smoothed L0 norm technique in order to directly solve the L0 norm constrained problem. Our method employs a smoothing function to make the L0 norm continuous. Extensive experiments on various datasets, including normal human data, isolated canine data, and WPW syndrome data, were conducted to validate our method. Epicardial potentials mapped during pacing were also reconstructed and visualized on the heart surface. Experimental results show that the proposed method reconstructs more accurate epicardial potentials compared with L1 norm and L2 norm based methods, demonstrating that smoothed L0 norm is a promising method for the noninvasive estimation of epicardial potentials.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319245706
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 5 Oct 20159 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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