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Propagation pattern analysis during atrial fibrillation based on sparse modeling

Richter, Ulrike LU ; Faes, Luca; Ravelli, Flavia and Sörnmo, Leif LU (2012) In IEEE Transactions on Biomedical Engineering 59(5). p.1319-1328
Abstract (Swedish)
Abstract in Undetermined

In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated... (More)
Abstract in Undetermined

In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and 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 both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Atrial fibrillation (AF), electrogram, group least absolute selection and shrinkage operator (LASSO), partial directed coherence (PDC), propagation pattern analysis
in
IEEE Transactions on Biomedical Engineering
volume
59
issue
5
pages
1319 - 1328
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000303201000013
  • scopus:84860375760
ISSN
0018-9294
DOI
10.1109/TBME.2012.2187054
language
English
LU publication?
yes
id
ab0a4149-c1ec-4311-9aa4-f5134474bae1 (old id 2293462)
date added to LUP
2012-01-12 10:34:34
date last changed
2017-05-28 03:53:42
@article{ab0a4149-c1ec-4311-9aa4-f5134474bae1,
  abstract     = {<b>Abstract in Undetermined</b><br/><br>
In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and 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 both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption.},
  author       = {Richter, Ulrike and Faes, Luca and Ravelli, Flavia and Sörnmo, Leif},
  issn         = {0018-9294},
  keyword      = {Atrial fibrillation (AF),electrogram,group least absolute selection and shrinkage operator (LASSO),partial directed coherence (PDC),propagation pattern analysis},
  language     = {eng},
  number       = {5},
  pages        = {1319--1328},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Biomedical Engineering},
  title        = {Propagation pattern analysis during atrial fibrillation based on sparse modeling},
  url          = {http://dx.doi.org/10.1109/TBME.2012.2187054},
  volume       = {59},
  year         = {2012},
}