248 research outputs found
Multiple mechanisms of spiral wave breakup in a model of cardiac electrical activity
It has become widely accepted that the most dangerous cardiac arrhythmias are
due to re- entrant waves, i.e., electrical wave(s) that re-circulate repeatedly
throughout the tissue at a higher frequency than the waves produced by the
heart's natural pacemaker (sinoatrial node). However, the complicated structure
of cardiac tissue, as well as the complex ionic currents in the cell, has made
it extremely difficult to pinpoint the detailed mechanisms of these
life-threatening reentrant arrhythmias. A simplified ionic model of the cardiac
action potential (AP), which can be fitted to a wide variety of experimentally
and numerically obtained mesoscopic characteristics of cardiac tissue such as
AP shape and restitution of AP duration and conduction velocity, is used to
explain many different mechanisms of spiral wave breakup which in principle can
occur in cardiac tissue. Some, but not all, of these mechanisms have been
observed before using other models; therefore, the purpose of this paper is to
demonstrate them using just one framework model and to explain the different
parameter regimes or physiological properties necessary for each mechanism
(such as high or low excitability, corresponding to normal or ischemic tissue,
spiral tip trajectory types, and tissue structures such as rotational
anisotropy and periodic boundary conditions). Each mechanism is compared with
data from other ionic models or experiments to illustrate that they are not
model-specific phenomena. The fact that many different breakup mechanisms exist
has important implications for antiarrhythmic drug design and for comparisons
of fibrillation experiments using different species, electromechanical
uncoupling drugs, and initiation protocols.Comment: 128 pages, 42 figures (29 color, 13 b&w
Reconstructing Cardiac Electrical Excitations from Optical Mapping Recordings
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
movie
Employing Gaussian process priors for studying spatial variation in the parameters of a cardiac action potential model
Cardiac cells exhibit variability in the shape and duration of their action
potentials in space within a single individual. To create a mathematical model
of cardiac action potentials (AP) which captures this spatial variability and
also allows for rigorous uncertainty quantification regarding within-tissue
spatial correlation structure, we developed a novel hierarchical Bayesian model
making use of a latent Gaussian process prior on the parameters of a simplified
cardiac AP model which is used to map forcing behavior to observed voltage
signals. This model allows for prediction of cardiac electrophysiological
dynamics at new points in space and also allows for reconstruction of surface
electrical dynamics with a relatively small number of spatial observation
points. Furthermore, we make use of Markov chain Monte Carlo methods via the
Stan modeling framework for parameter estimation. We employ a synthetic data
case study oriented around the reconstruction of a sparsely-observed spatial
parameter surface to highlight how this approach can be used for spatial or
spatiotemporal analyses of cardiac electrophysiology
Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium
Over the past two decades there has been a steady trend towards the
development of realistic models of cardiac conduction with increasing levels of
detail. However, making models more realistic complicates their personalization
and use in clinical practice due to limited availability of tissue and cellular
scale data. One such limitation is obtaining information about myocardial fiber
organization in the clinical setting. In this study, we investigated a chimeric
model of the left atrium utilizing clinically derived patient-specific atrial
geometry and a realistic, yet foreign for a given patient fiber organization.
We discovered that even significant variability of fiber organization had a
relatively small effect on the spatio-temporal activation pattern during
regular pacing. For a given pacing site, the activation maps were very similar
across all fiber organizations tested
- …