title: Physiology-based regularization of the electrocardiographic inverse problem.
abstract: 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.
authors.affiliation: Department of Data Science and Knowledge Engineering and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands. m.cluitmans@maastrichtuniversity.nl.
authors.forename: Matthijs J M
authors.identifier: http://orcid.org/0000-0002-3456-7668
authors.initials: MJM
authors.lastname: Cluitmans
authors.affiliation: Department of Data Science and Knowledge Engineering and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
authors.forename: Michael
authors.initials: M
authors.lastname: Clerx
authors.affiliation: Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
authors.forename: Nele
authors.initials: N
authors.lastname: Vandersickel
authors.affiliation: Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands.
authors.forename: Ralf L M
authors.initials: RLM
authors.lastname: Peeters
authors.affiliation: CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
authors.forename: Paul G A
authors.initials: PGA
authors.lastname: Volders
authors.affiliation: Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands.
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Westra
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004285:Dogs; D004562:Electrocardiography / Q000379:methods*; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Cardiology; Electrocardiographic imaging; Electrocardiography