Propagation pattern analysis during atrial fibrillation based on sparse modeling
(2012) In IEEE Transactions on Biomedical Engineering 59(5). p.1319-1328- Abstract
- 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... (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:
https://lup.lub.lu.se/record/2293462
- author
- Richter, Ulrike LU ; Faes, Luca ; Ravelli, Flavia and Sörnmo, Leif LU
- organization
- publishing date
- 2012
- 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
- pmid:22328169
- ISSN
- 1558-2531
- DOI
- 10.1109/TBME.2012.2187054
- language
- English
- LU publication?
- yes
- id
- ab0a4149-c1ec-4311-9aa4-f5134474bae1 (old id 2293462)
- date added to LUP
- 2016-04-01 13:08:23
- date last changed
- 2022-03-06 04:14:21
@article{ab0a4149-c1ec-4311-9aa4-f5134474bae1,
abstract = {{Abstract in Undetermined<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 = {{1558-2531}},
keywords = {{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}},
doi = {{10.1109/TBME.2012.2187054}},
volume = {{59}},
year = {{2012}},
}