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Reconstructing cardiac electrical excitations from optical mapping recordings

Marcotte, C. D.; Hoffman, M. J.; Fenton, F. H.; Cherry, E. M.

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M. J. Hoffman

F. H. Fenton

E. M. Cherry


The reconstruction of electrical excitation patterns through the unobserved depth of the tissue is essential to realizing the potential of computational models in cardiac medicine. We have utilized experimental optical-mapping recordings of cardiac electrical excitation on the epicardial and endocardial surfaces of a canine ventricle as observations directing a local ensemble transform Kalman filter data assimilation scheme. We demonstrate that the inclusion of explicit information about the stimulation protocol can marginally improve the confidence of the ensemble reconstruction and the reliability of the assimilation over time. Likewise, we consider the efficacy of stochastic modeling additions to the assimilation scheme in the context of experimentally derived observation sets. Approximation error is addressed at both the observation and modeling stages through the uncertainty of observations and the specification of the model used in the assimilation ensemble. We find that perturbative modifications to the observations have marginal to deleterious effects on the accuracy and robustness of the state reconstruction. Furthermore, we find that incorporating additional information from the observations into the model itself (in the case of stimulus and stochastic currents) has a marginal improvement on the reconstruction accuracy over a fully autonomous model, while complicating the model itself and thus introducing potential for new types of model errors. That the inclusion of explicit modeling information has negligible to negative effects on the reconstruction implies the need for new avenues for optimization of data assimilation schemes applied to cardiac electrical excitation.


Marcotte, C. D., Hoffman, M. J., Fenton, F. H., & Cherry, E. M. (2023). Reconstructing cardiac electrical excitations from optical mapping recordings. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(9), Article 093141.

Journal Article Type Article
Acceptance Date Sep 1, 2023
Online Publication Date Sep 27, 2023
Publication Date 2023-09
Deposit Date Sep 28, 2023
Publicly Available Date Sep 28, 2023
Journal Chaos: An Interdisciplinary Journal of Nonlinear Science
Print ISSN 1054-1500
Electronic ISSN 1089-7682
Publisher American Institute of Physics
Peer Reviewed Peer Reviewed
Volume 33
Issue 9
Article Number 093141
Keywords Heart, Heart Ventricles, Electricity, Endocardium, Dogs, Animals, Reproducibility of Results
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