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Propagation pattern analysis during atrial fibrillation based on sparse modeling.

2012

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 si…

Normalization (statistics)Computer scienceAtrial fibrillation (AF)Biomedical EngineeringSignalPattern Recognition AutomatedElectrocardiographyelectrogramgroup least absolute selection and shrinkage operator (LASSO)Operator (computer programming)StatisticsAtrial FibrillationHumansComputer SimulationSelection (genetic algorithm)ShrinkageSignal processingNoise (signal processing)partial directed coherence (PDC)Models CardiovascularSignal Processing Computer-Assistedpropagation pattern analysiFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPattern recognition (psychology)AlgorithmAlgorithmsIEEE transactions on bio-medical engineering
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