A fast algorithm for spatiotemporal signals recovery using arbitrary dictionaries with application to electrocardiographic imaging

Biomed Phys Eng Express. 2022 Sep 13;8(6). doi: 10.1088/2057-1976/ac835b.

Abstract

This paper presents a method to solve a linear regression problem subject to grouplassoand ridge penalisation when the model has a Kronecker structure. This model was developed to solve the inverse problem of electrocardiography using sparse signal representation over a redundant dictionary or frame. The optimisation algorithm was performed using the block coordinate descent and proximal gradient descent methods. The explicit computation of the underlying Kronecker structure in the regression was avoided, reducing space and temporal complexity. We developed an algorithm that supports the use of arbitrary dictionaries to obtain solutions and allows a flexible group distribution.

Keywords: ECGI; Kronecker product; group lasso; sparse regularization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Diagnostic Imaging
  • Electrocardiography*
  • Linear Models