ZEUS: Numerical methods to detect quasi-particles describing excitable media

Many complex systems with societal relevance are excitable, including brain tissue, cardiac tissue, heat waves, and epidemic spread. In cardiac tissue, arrhythmias often arise when electrical conduction is blocked by incomplete recovery after a previous stimulation. We recently presented a topological theory for excitable media that also captures conduction blocks. Therein, points where a wave front or a wave back spatially connects to a conduction block were shown to be topologically preserved, and denoted heads and tails, respectively. Here, we introduce algorithms to automatically localize heads and tails in excitation patterns on triangle meshes. We describe two variants, depending on the co-dimension of the forbidden zone in state space. As a result, the conduction block region is rendered either as a line of conduction block or an extended conduction block region. A key operation is to apply a bitwise OR operation onto states of a local vertex according to a partitioning into forbidden zone (Z), excited (E), unexcited (U), and their respective signed variants (S), which we abbreviate as ZEUS methods. The methods are applied to visualize heads, tails, and conduction blocks in simulated data and optical voltage mapping recordings of ventricular tachycardia in rabbit hearts. We compare the outcome to classical phase singularity analysis. Theoretical relations between the different options and advantages of each method are discussed. Robust algorithms are presented and made publicly available to identify certain topologically preserved points in excitation patterns. These methods can be used for automated analysis and classification.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-12-01
DOI
https://doi.org/10.1063/5.0288713
Primary Topic
Topological and Geometric Data Analysis
Type
article
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article

ZEUS: Numerical methods to detect quasi-particles describing excitable media

Hans Dierckx, Desmond Kabus, Elena G. Tolkacheva, Aaron Gobeyn
Chaos An Interdisciplinary Journal of Nonlinear Science
Topological and Geometric Data Analysis
article

ZEUS: Numerical methods to detect quasi-particles describing excitable media

Hans Dierckx, Desmond Kabus, Elena G. Tolkacheva, Aaron Gobeyn
article en

Abstract

Many complex systems with societal relevance are excitable, including brain tissue, cardiac tissue, heat waves, and epidemic spread. In cardiac tissue, arrhythmias often arise when electrical conduction is blocked by incomplete recovery after a previous stimulation. We recently presented a topological theory for excitable media that also captures conduction blocks. Therein, points where a wave front or a wave back spatially connects to a conduction block were shown to be topologically preserved, and denoted heads and tails, respectively. Here, we introduce algorithms to automatically localize heads and tails in excitation patterns on triangle meshes. We describe two variants, depending on the co-dimension of the forbidden zone in state space. As a result, the conduction block region is rendered either as a line of conduction block or an extended conduction block region. A key operation is to apply a bitwise OR operation onto states of a local vertex according to a partitioning into forbidden zone (Z), excited (E), unexcited (U), and their respective signed variants (S), which we abbreviate as ZEUS methods. The methods are applied to visualize heads, tails, and conduction blocks in simulated data and optical voltage mapping recordings of ventricular tachycardia in rabbit hearts. We compare the outcome to classical phase singularity analysis. Theoretical relations between the different options and advantages of each method are discussed. Robust algorithms are presented and made publicly available to identify certain topologically preserved points in excitation patterns. These methods can be used for automated analysis and classification.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(12)
University of Minnesota (US), Leiden University Medical Center (NL), Technische Universität Darmstadt (DE), Vlakwa (BE)
Openalex Percentile: Top 62%
Topological and Geometric Data Analysis
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