Propagation pattern analysis during atrial fibrillation based on sparse modeling
Författare
Summary, in English
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.
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.
Avdelning/ar
Publiceringsår
2012
Språk
Engelska
Sidor
1319-1328
Publikation/Tidskrift/Serie
IEEE Transactions on Biomedical Engineering
Volym
59
Issue
5
Dokumenttyp
Artikel i tidskrift
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Medical Engineering
Nyckelord
- Atrial fibrillation (AF)
- electrogram
- group least absolute selection and shrinkage operator (LASSO)
- partial directed coherence (PDC)
- propagation pattern analysis
Status
Published
Forskningsgrupp
- Signal Processing
ISBN/ISSN/Övrigt
- ISSN: 1558-2531