P919Heart rhythm model and simulation of electrophysiological studies and high-frequency ablations

Schalk, Marco; Heinke, Matthias; Echle, Reinhard

doi:10.1093/ehjci/eux151.101

Cited by 1 publication

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“…We focused here on simple propagation, nevertheless, our view is that in fibrillatory rhythms the accurate estimation of the underlying transmembrane potentials should be strongly informative, as far as their mechanisms still remains unknown. A great amount of knowledge on fibrillatory rhythms has been established by detailed computer simulations on transmembrane potentials and ionic currents [ 62 – 64 ]. However, it is desirable to improve the method in more electrophysiologically simple situations before moving to complex mechanisms.…”

Section: Discussionmentioning

confidence: 99%

A new approach to the intracardiac inverse problem using Laplacian distance kernel

et al. 2018

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BackgroundThe inverse problem in electrophysiology consists of the accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at short distances and from outside the heart. This estimation can provide an image with relevant knowledge on arrhythmia mechanisms for the clinical practice. Methods based on truncated singular value decomposition (TSVD) and regularized least squares require a matrix inversion, which limits their resolution due to the unavoidable low-pass filter effect of the Tikhonov regularization techniques.MethodsWe propose to use, for the first time, a Mercer’s kernel given by the Laplacian of the distance in the quasielectrostatic field equations, hence providing a Support Vector Regression (SVR) formulation by following the principles of the Dual Signal Model (DSM) principles for creating kernel algorithms.ResultsSimulations in one- and two-dimensional models show the performance of our Laplacian distance kernel technique versus several conventional methods. Firstly, the one-dimensional model is adjusted for yielding recorded electrograms, similar to the ones that are usually observed in electrophysiological studies, and suitable strategy is designed for the free-parameter search. Secondly, simulations both in one- and two-dimensional models show larger noise sensitivity in the estimated transfer matrix than in the observation measurements, and DSM−SVR is shown to be more robust to noisy transfer matrix than TSVD.ConclusionThese results suggest that our proposed DSM−SVR with Laplacian distance kernel can be an efficient alternative to improve the resolution in current and emerging intracardiac imaging systems.

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Section: Discussionmentioning

confidence: 99%