Affiliations: [a] Department of Circuits and Systems Engineering, Technical University of Madrid, Madrid, Spain | [b] Department of Signal Theory and Communications, University Carlos III de Madrid, Leganés, Spain | [c] Department of Electrical and Electronic Engineering, Shamoon College of Engineering, Ashdod, Israel | [d] Department of Communications Engineering, University of Cantabria, Santander, Spain
Correspondence:
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Corresponding author: David Luengo, Department of Circuits and Systems Engineering, Technical University of Madrid, Ctra. de Valencia, km. 7, 28031 Madrid, Spain. E-mail: [email protected].
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.
