Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO

Ulrike Richter, Luca Faes, Flavia Ravelli, Leif Sörnmo

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.20±0.04 for LS estimation to 0.03±0.01 for aLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. The error reduction was more pronounced for short data segments. Propagation patterns were also studied on intrac-ardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption.
Original languageEnglish
Title of host publicationEngineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Pages5535-5538
Volume3
DOIs
Publication statusPublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Boston, United States
Duration: 2011 Aug 302011 Sep 3

Publication series

Name
Volume3
ISSN (Print)1557-170X

Conference

Conference33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/TerritoryUnited States
CityBoston
Period2011/08/302011/09/03

Subject classification (UKÄ)

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

  • Accuracy
  • Adaptation models
  • Catheters
  • Couplings
  • Estimation
  • Pattern analysis
  • Time series analysis

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