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Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology.

Krishnamoorthi S, Perotti LE, Borgstrom NP, Ajijola OA, Frid A, Ponnaluri AV, Weiss JN, Qu Z, Klug WS, Ennis DB, Garfinkel A - PLoS ONE (2014)

Bottom Line: We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time.We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential.Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

No MeSH data available.


Related in: MedlinePlus

Voltage contour plots obtained with ventricular models using different tensor interpolation schemes.From top to bottom: Geolox, Log Euclidean, Euclidean, and Nearest Neighbor. Comparison of the voltage propagation is reported here at three time steps. From left to right: , , and .
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pone-0114494-g007: Voltage contour plots obtained with ventricular models using different tensor interpolation schemes.From top to bottom: Geolox, Log Euclidean, Euclidean, and Nearest Neighbor. Comparison of the voltage propagation is reported here at three time steps. From left to right: , , and .

Mentions: Activation sequences for models constructed with Geolox, log-Euclidean, Euclidean, and Nearest Neighbor tensor interpolation schemes were computed over a single heartbeat. The voltage contours (Fig. 7) for all of the methods showed negligible differences throughout the beat cycle. RMS differences among the nodal voltages at every time step were at most (data not shown). This maximum discrepancy was found between the models using Geolox and Nearest Neighbor interpolation. The maximum RMSD between Geolox and Euclidean interpolations was even smaller at . The maximum RMSD between Geolox and log-Euclidean interpolations was also . Among the three different Purkinje geometry models, the simulation with high PMJs shows minimal difference between different tensor interpolation schemes. The smallest differences in maximum RMSD were obtained when comparing Geolox, log-Euclidean, and Euclidean tensor interpolation methods, which show minimal differences in the primary eigenvector orientation [22]. However, although the Nearest Neighbor interpolation method leads to a relatively small RMSD due to the smoothing effects of diffusive coupling, it should be avoided due to large biases associated with interpolating the primary eigenvector [42]. If other tensor attributes (fractional anisotropy, etc.) were used in the computational model, then further evaluation would be needed.


Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology.

Krishnamoorthi S, Perotti LE, Borgstrom NP, Ajijola OA, Frid A, Ponnaluri AV, Weiss JN, Qu Z, Klug WS, Ennis DB, Garfinkel A - PLoS ONE (2014)

Voltage contour plots obtained with ventricular models using different tensor interpolation schemes.From top to bottom: Geolox, Log Euclidean, Euclidean, and Nearest Neighbor. Comparison of the voltage propagation is reported here at three time steps. From left to right: , , and .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4262432&req=5

pone-0114494-g007: Voltage contour plots obtained with ventricular models using different tensor interpolation schemes.From top to bottom: Geolox, Log Euclidean, Euclidean, and Nearest Neighbor. Comparison of the voltage propagation is reported here at three time steps. From left to right: , , and .
Mentions: Activation sequences for models constructed with Geolox, log-Euclidean, Euclidean, and Nearest Neighbor tensor interpolation schemes were computed over a single heartbeat. The voltage contours (Fig. 7) for all of the methods showed negligible differences throughout the beat cycle. RMS differences among the nodal voltages at every time step were at most (data not shown). This maximum discrepancy was found between the models using Geolox and Nearest Neighbor interpolation. The maximum RMSD between Geolox and Euclidean interpolations was even smaller at . The maximum RMSD between Geolox and log-Euclidean interpolations was also . Among the three different Purkinje geometry models, the simulation with high PMJs shows minimal difference between different tensor interpolation schemes. The smallest differences in maximum RMSD were obtained when comparing Geolox, log-Euclidean, and Euclidean tensor interpolation methods, which show minimal differences in the primary eigenvector orientation [22]. However, although the Nearest Neighbor interpolation method leads to a relatively small RMSD due to the smoothing effects of diffusive coupling, it should be avoided due to large biases associated with interpolating the primary eigenvector [42]. If other tensor attributes (fractional anisotropy, etc.) were used in the computational model, then further evaluation would be needed.

Bottom Line: We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time.We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential.Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.

No MeSH data available.


Related in: MedlinePlus