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

Richter, Ulrike LU ; Faes, Luca; Ravelli, Flavia and Sörnmo, Leif LU (2011) 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE 3. p.5535-5538
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Accuracy, Adaptation models, Catheters, Couplings, Estimation, Pattern analysis, Time series analysis
in
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
volume
3
pages
5535 - 5538
conference name
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
external identifiers
  • WOS:000298810004117
  • Scopus:84055192717
ISSN
1557-170X
ISBN
978-1-4244-4121-1
DOI
10.1109/IEMBS.2011.6091412
language
English
LU publication?
yes
id
c2b885ed-5011-48dd-a487-359f3e780aa1 (old id 2293380)
date added to LUP
2012-01-12 10:32:07
date last changed
2016-10-13 04:31:16
@misc{c2b885ed-5011-48dd-a487-359f3e780aa1,
  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.},
  author       = {Richter, Ulrike and Faes, Luca and Ravelli, Flavia and Sörnmo, Leif},
  isbn         = {978-1-4244-4121-1},
  issn         = {1557-170X},
  keyword      = {Accuracy,Adaptation models,Catheters,Couplings,Estimation,Pattern analysis,Time series analysis},
  language     = {eng},
  pages        = {5535--5538},
  series       = {Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE},
  title        = {Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO},
  url          = {http://dx.doi.org/10.1109/IEMBS.2011.6091412},
  volume       = {3},
  year         = {2011},
}