Physiology-based regularization of the electrocardiographic inverse problem.

The inverse problem of electrocardiography aims at noninvasively reconstructing electrical activity of the heart from recorded body-surface electrocardiograms. A crucial step is regularization, which deals with ill-posedness of the problem by imposing constraints on the possible solutions. We develo...

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Publicado en:Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1353 - 1366
Autores principales: Cluitmans, Matthijs, Clerx, Michael, Vandersickel, Nele, Peeters, Ralf, Volders, Paul, Westra, Ronald, Cluitmans, Matthijs J M, Peeters, Ralf L M, Volders, Paul G A, Westra, Ronald L
Formato: equations & formulas pictorial research tables/charts tracings Journal Article
Publicado: Springer Nature Aug2017
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Physiology-based regularization of the electrocardiographic inverse problem.
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        au:
          Cluitmans, Matthijs
          Clerx, Michael
          Vandersickel, Nele
          Peeters, Ralf
          Volders, Paul
          Westra, Ronald
          Cluitmans, Matthijs J M
          Peeters, Ralf L M
          Volders, Paul G A
          Westra, Ronald L
        affil: Department of Data Science and Knowledge Engineering and CARIM School for Cardiovascular Diseases , Maastricht University , Maastricht The Netherlands
      sug:
        subj:
          Action Potentials Physiology
          Heart Conduction System Physiology
          Models, Biological
          Diagnosis, Computer Assisted Methods
          Body Surface Potential Mapping Methods
          Electrocardiography Methods
          Algorithms
          Dogs
          Reproducibility of Results
          Sensitivity and Specificity
          Animals
          Computer Simulation
          Animal Studies
      ab: The inverse problem of electrocardiography aims at noninvasively reconstructing electrical activity of the heart from recorded body-surface electrocardiograms. A crucial step is regularization, which deals with ill-posedness of the problem by imposing constraints on the possible solutions. We developed a regularization method that includes electrophysiological input. Body-surface potentials are recorded and a computed tomography scan is performed to obtain the torso-heart geometry. Propagating waveforms originating from several positions at the heart are simulated and used to generate a set of basis vectors representing spatial distributions of potentials on the heart surface. The real heart-surface potentials are then reconstructed from the recorded body-surface potentials by finding a sparse representation in terms of this basis. This method, which we named 'physiology-based regularization' (PBR), was compared to traditional Tikhonov regularization and validated using in vivo recordings in dogs. PBR recovered details of heart-surface electrograms that were lost with traditional regularization, attained higher correlation coefficients and led to improved estimation of recovery times. The best results were obtained by including approximate knowledge about the beat origin in the PBR basis.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
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        Journal Article
      ougenre: Article
    language: English
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