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Robust data assimilation with noise: Applications to cardiac dynamics

Published:2021-01 Issue:1 Volume:31 Page:013118
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Marcotte Christopher D.¹^ORCID,Fenton Flavio H.²^ORCID,Hoffman Matthew J.³^ORCID,Cherry Elizabeth M.¹^ORCID

Affiliation:

1. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

2. School of Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

3. School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York 14623, USA

Funder

National Science Foundation

National Heart, Lung, and Blood Institute

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0033539

Reference67 articles.

1. Reconstructing three-dimensional reentrant cardiac electrical wave dynamics using data assimilation

2. Effects of model error on cardiac electrical wave state reconstruction using data assimilation

3. Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics

4. The dynamics of three-dimensional scroll waves in excitable media

5. Visualization of spiral and scroll waves in simulated and experimental cardiac tissue

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1. T-wave inversion through inhomogeneous voltage diffusion within the FK3V cardiac model;Chaos: An Interdisciplinary Journal of Nonlinear Science;2024-04-01

2. Reconstructing cardiac electrical excitations from optical mapping recordings;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-09-01

3. Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-01-01

4. A Simulation Study of the Effects of His Bundle Pacing in Left Bundle Branch Block;Medical Engineering & Physics;2022-09

5. A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition;Biophysical Journal;2022-08

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