Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns

The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media

“…Also, higher-dimensional rotors waves were simulated and the emerging super-filaments were detected using Ithildin [27]. In the creation of novel data-driven tissue models using state space expansion, Ithildin was used to generate synthetic training data sets [22].…”

“…detected using Ithildin [27]. In the creation of novel data-driven tissue models using state space expansion, Ithildin was used to generate synthetic training data sets [22].…”

“…While we have selected these file formats to be easy to read using a wide variety of software, we have also developed the Python module for Ithildin to facilitate interacting with the results of an Ithildin simulation and converting them to a variety of file formats, for instance writing files in the extensible data model and format (XDMF) that can be used to view simulation results in ParaView [14,17,22,23]. The Python module also offers post-processing and analysis methods, for instance the computation of action potential duration (APD), conduction velocity (CV), various phases, phase defect DRAFT from 2024-04-29 11:19 UTC+02 7/31 detection, functions acting on filaments and filament trajectories, and several plotting functions [14,22].…”