English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Predicting the duration of chaotic transients in excitable media

MPS-Authors
/persons/resource/persons250628

Aron,  Marcel
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons217528

Lilienkamp,  Thomas
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173583

Luther,  Stefan
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173613

Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Aron, M., Lilienkamp, T., Luther, S., & Parlitz, U. (2021). Predicting the duration of chaotic transients in excitable media. Journal of Physics: Complexity, 2: 035016. doi:10.1088/2632-072X/abf752.


Cite as: https://hdl.handle.net/21.11116/0000-0008-DB3B-0
Abstract
The spatiotemporal dynamics of excitable media may exhibit chaotic transients. We investigate this
transient chaos in the 2D Fenton–Karma model describing the propagation of electrical excitation
waves in cardiac tissue and compute the average duration of chaotic transients in dependence on
model parameter values. Furthermore, other characteristics like the dominant frequency, the size of
the excitable gap, pseudo ECGs, the number of phase singularities and parameters characterizing
the action potential duration restitution curve are determined and it is shown that these quantities
can be used to predict the average transient time using polynomial regression.