Numerical methods for the detection of phase defect structures in excitable media

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Electrical waves that rotate in the heart organize dangerous cardiac arrhythmias. Finding the region around which such rotation occurs is one of the most important practical questions for arrhythmia management. For many years, the main method for finding such regions was so-called phase mapping, in which a continuous phase was assigned to points in the heart based on their excitation status and defining the rotation region as a point of phase singularity. Recent analysis, however, showed that in many rotation regimes there exist phase discontinuities and the region of rotation must be defined not as a point of phase singularity, but as a phase defect line. In this paper, we use this novel methodology and perform a comparative study of three different phase definitions applied to in silico data and to experimental data obtained from optical voltage mapping experiments on monolayers of human atrial myocytes. We introduce new phase defect detection algorithms and compare them with those that appeared in literature already. We find that the phase definition is more important than the algorithm to identify sudden spatial phase variations. Sharp phase defect lines can be obtained from a phase derived from local activation times observed during one cycle of arrhythmia. Alternatively, similar quality can be obtained from a reparameterization of the classical phase obtained from observation of a single timeframe of transmembrane potential. We found that the phase defect line length was (35.9 ± 6.2) mm in the Fenton-Karma model and (4.01 ± 0.55) mm in cardiac human atrial myocyte monolayers. As local activation times are obtained during standard clinical cardiac mapping, the methods are also suitable to be applied to clinical datasets. All studied methods are publicly available and can be downloaded from an institutional web-server.

Authors 5

1. Desmond Kabus Aachen Laboratory of Experimental Cardiology

   Leiden University Medical Center · KU Leuven

   Affiliation as printed

   Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Kortrijk, Belgium

   Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Leiden, The Netherlands

   iSi Health, KU Leuven Institute of Physics-based Modeling for In Silico Health, Leuven, Belgium

2. Louise Arno

   KU Leuven

   Affiliation as printed

   Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Kortrijk, Belgium

   iSi Health, KU Leuven Institute of Physics-based Modeling for In Silico Health, Leuven, Belgium

3. Lore Leenknegt

   KU Leuven

   Affiliation as printed

   Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Kortrijk, Belgium

   iSi Health, KU Leuven Institute of Physics-based Modeling for In Silico Health, Leuven, Belgium

4. Alexander V. Panfilov

   Ural Federal University · Sechenov University · Ghent University

   Affiliation as printed

   Department of Physics and Astronomy, Ghent University, Ghent, Belgium

   Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia

   World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov University, Moscow, Russia

   World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov University, Moscow, Russia

5. Hans Dierckx corresponding

   KU Leuven

   Affiliation as printed

   Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Kortrijk, Belgium

   iSi Health, KU Leuven Institute of Physics-based Modeling for In Silico Health, Leuven, Belgium