Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO
(2011) 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2293380
- author
- Richter, Ulrike LU ; Faes, Luca ; Ravelli, Flavia and Sörnmo, Leif LU
- organization
- publishing date
- 2011
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Accuracy, Adaptation models, Catheters, Couplings, Estimation, Pattern analysis, Time series analysis
- host publication
- 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
- conference location
- Boston, United States
- conference dates
- 2011-08-30 - 2011-09-03
- external identifiers
-
- wos:000298810004117
- scopus:84055192717
- pmid:22255592
- 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
- 2016-04-04 09:28:36
- date last changed
- 2022-01-29 18:05:29
@inproceedings{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}}, booktitle = {{Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE}}, isbn = {{978-1-4244-4121-1}}, issn = {{1557-170X}}, keywords = {{Accuracy; Adaptation models; Catheters; Couplings; Estimation; Pattern analysis; Time series analysis}}, language = {{eng}}, pages = {{5535--5538}}, title = {{Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO}}, url = {{http://dx.doi.org/10.1109/IEMBS.2011.6091412}}, doi = {{10.1109/IEMBS.2011.6091412}}, volume = {{3}}, year = {{2011}}, }