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Hierarchical Multiple-Model Bayesian Approach to Transmural Electrophysiological Imaging

  • Azar Rahimi
  • Jingjia Xu
  • Linwei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Noninvasive electrophysiological (EP) imaging of the heart aims to mathematically reconstruct the spatiotemporal dynamics of cardiac current sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, this approach enforces the source distribution to follow a pre-assumed spatial structure that does not always match the varying spatiotemporal distribution of current sources. We propose a hierarchical Bayesian approach to transmural EP imaging that employs a continuous combination of multiple models, each reflecting a specific spatial property for current sources. Multiple models are incorporated as an Lp-norm prior for current sources, where p is an unknown hyperparameter with a prior probabilistic distribution. The current source estimation is obtained as an optimally weighted combination of solutions across all models, the weight being determined from the posterior distribution of p inferred from ECG data. The accuracy of our approach is assessed in a set of synthetic and real-data experiments on human heart-torso models. While the use of fixed models (L1- and L2-norm) only properly recovers sources with specific structures, our method delivers consistent performance in reconstructing sources with various extents and structures.

Keywords

Transmural electrophysiological imaging Lp-norm regularization multiple-model estimation Bayesian inference 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Azar Rahimi
    • 1
  • Jingjia Xu
    • 1
  • Linwei Wang
    • 1
  1. 1.Rochester Institute of TechnologyRochesterUSA

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