title: Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.
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 intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption.
authors.affiliation: Signal Processing Group, Department of Electrical and Information Technology, Lund University. ulrike.richter@med.lu.se
authors.forename: Ulrike
authors.initials: U
authors.lastname: Richter
authors.forename: Luca
authors.initials: L
authors.lastname: Faes
authors.forename: Flavia
authors.initials: F
authors.lastname: Ravelli
authors.forename: Leif
authors.initials: L
authors.lastname: Sörnmo
mesh_terms: D000465:Algorithms; D000818:Animals; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006329:Heart Conduction System / Q000503:physiopathology*; D006801:Humans; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity