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

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Author

Summary, in English

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.

Publishing year

2011

Language

English

Pages

5535-5538

Publication/Series

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Volume

3

Document type

Conference paper

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

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

Conference name

33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Conference date

2011-08-30 - 2011-09-03

Conference place

Boston, United States

Status

Published

Research group

  • Signal Processing

ISBN/ISSN/Other

  • ISSN: 1557-170X
  • ISBN: 978-1-4244-4121-1