Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO
Författare
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.
Publiceringsår
2011
Språk
Engelska
Sidor
5535-5538
Publikation/Tidskrift/Serie
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Volym
3
Dokumenttyp
Konferensbidrag
Ämne
- Electrical Engineering, Electronic Engineering, Information Engineering
Nyckelord
- 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
Forskningsgrupp
- Signal Processing
ISBN/ISSN/Övrigt
- ISSN: 1557-170X
- ISBN: 978-1-4244-4121-1