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Abstract

The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like
ventricular fbrillation is governed by the dynamics of vortex-like spiral or scroll waves. The
organizing centers of these waves are called wave tips (2D) or flaments (3D) and they play a key role
in understanding and controlling the complex and chaotic electrical dynamics. Therefore, in many
experimental and numerical setups it is required to detect the tips of the observed spiral waves. Most
of the currently used methods signifcantly sufer from the infuence of noise and are often adjusted
to a specifc situation (e.g. a specifc numerical cardiac cell model). In this study, we use a specifc type
of deep neural networks (UNet), for detecting spiral wave tips and show that this approach is robust
against the infuence of intermediate noise levels. Furthermore, we demonstrate that if the UNet
is trained with a pool of numerical cell models, spiral wave tips in unknown cell models can also be
detected reliably, suggesting that the UNet can in some sense learn the concept of spiral wave tips in a
general way, and thus could also be used in experimental situations in the future (ex-vivo, cell-culture
or optogenetic experiments).