Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation Articles uri icon

publication date

  • January 2015

start page

  • 71

end page

  • 85

issue

  • 1

volume

  • 22

International Standard Serial Number (ISSN)

  • 1069-2509

Electronic International Standard Serial Number (EISSN)

  • 1875-8835

abstract

  • The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.

keywords

  • biomedical signal processing ; atrial fibrillation electrograms; sparsity-aware learning ; lasso regularization; spectral analysis