Detecting spiral wave tips using deep learning

Abstract The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like ventricular fibrillation is governed by the dynamics of vortex-like spiral or scroll waves. The organizing centers of these waves are called wave tips (2D) or filaments (3D) and they play a key...

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Autores principales:	Henning Lilienkamp, Thomas Lilienkamp
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4293d6cc837445a68f2309d1855ed03d
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id	oai:doaj.org-article:4293d6cc837445a68f2309d1855ed03d
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spelling	oai:doaj.org-article:4293d6cc837445a68f2309d1855ed03d2021-12-02T18:37:10ZDetecting spiral wave tips using deep learning10.1038/s41598-021-99069-32045-2322https://doaj.org/article/4293d6cc837445a68f2309d1855ed03d2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99069-3https://doaj.org/toc/2045-2322Abstract The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like ventricular fibrillation is governed by the dynamics of vortex-like spiral or scroll waves. The organizing centers of these waves are called wave tips (2D) or filaments (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 significantly suffer from the influence of noise and are often adjusted to a specific situation (e.g. a specific numerical cardiac cell model). In this study, we use a specific type of deep neural networks (UNet), for detecting spiral wave tips and show that this approach is robust against the influence 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).Henning LilienkampThomas LilienkampNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Henning Lilienkamp Thomas Lilienkamp Detecting spiral wave tips using deep learning
description	Abstract The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like ventricular fibrillation is governed by the dynamics of vortex-like spiral or scroll waves. The organizing centers of these waves are called wave tips (2D) or filaments (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 significantly suffer from the influence of noise and are often adjusted to a specific situation (e.g. a specific numerical cardiac cell model). In this study, we use a specific type of deep neural networks (UNet), for detecting spiral wave tips and show that this approach is robust against the influence 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).
format	article
author	Henning Lilienkamp Thomas Lilienkamp
author_facet	Henning Lilienkamp Thomas Lilienkamp
author_sort	Henning Lilienkamp
title	Detecting spiral wave tips using deep learning
title_short	Detecting spiral wave tips using deep learning
title_full	Detecting spiral wave tips using deep learning
title_fullStr	Detecting spiral wave tips using deep learning
title_full_unstemmed	Detecting spiral wave tips using deep learning
title_sort	detecting spiral wave tips using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4293d6cc837445a68f2309d1855ed03d
work_keys_str_mv	AT henninglilienkamp detectingspiralwavetipsusingdeeplearning AT thomaslilienkamp detectingspiralwavetipsusingdeeplearning
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Detecting spiral wave tips using deep learning

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