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Directed Networks as a Novel Way to Describe and Analyze Cardiac Excitation: Directed Graph Mapping.

Authors
  • Vandersickel, Nele1
  • Van Nieuwenhuyse, Enid1
  • Van Cleemput, Nico2
  • Goedgebeur, Jan2, 3
  • El Haddad, Milad4
  • De Neve, Jan5
  • Demolder, Anthony4
  • Strisciuglio, Teresa6
  • Duytschaever, Mattias4, 6
  • Panfilov, Alexander V1, 7
  • 1 Department of Physics and Astronomy, Ghent University, Ghent, Belgium. , (Belgium)
  • 2 Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium. , (Belgium)
  • 3 Computer Science Department, University of Mons, Mons, Belgium. , (Belgium)
  • 4 Ghent University Hospital Heart Center, Ghent University, Ghent, Belgium. , (Belgium)
  • 5 Department of Data Analysis, Ghent University, Ghent, Belgium. , (Belgium)
  • 6 Cardiology Department, AZ Sint-Jan, Bruges, Belgium. , (Belgium)
  • 7 Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia.
Type
Published Article
Journal
Frontiers in Physiology
Publisher
Frontiers Media SA
Publication Date
Jan 01, 2019
Volume
10
Pages
1138–1138
Identifiers
DOI: 10.3389/fphys.2019.01138
PMID: 31551814
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood.

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