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 (LETKF) 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. Further, 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 error. 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.Comment: main text: 18 pages, 10 figures; supplement: 5 pages, 9 figures, 2
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