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Monodomain model

The monodomain model is a reduction of the bidomain model of the electrical propagation in myocardial tissue. The reduction comes from assuming that the intra- and extracellular domains have equal anisotropy ratios. Although not as physiologically accurate as the bidomain model, it is still adequate in some cases, and has reduced complexity.

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The monodomain model is a reduction of the bidomain model of the electrical propagation in myocardial tissue. The reduction comes from assuming that the intra- and extracellular domains have equal anisotropy ratios. Although not as physiologically accurate as the bidomain model, it is still adequate in some cases, and has reduced complexity.1

Formulation

Being T {\displaystyle \mathbb {T} } the spatial domain, and T {\displaystyle T} the final time, the monodomain model can be formulated as follows2 λ 1 + λ ( Σ i v ) = χ ( C m v t + I ion ) in  T × ( 0 , T ) , {\displaystyle {\frac {\lambda }{1+\lambda }}\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v\right)=\chi \left(C_{m}{\frac {\partial v}{\partial t}}+I_{\text{ion}}\right)\quad \quad {\text{in }}\mathbb {T} \times (0,T),}

where Σ i {\displaystyle \mathbf {\Sigma } _{i}} is the intracellular conductivity tensor, v {\displaystyle v} is the transmembrane potential, I ion {\displaystyle I_{\text{ion}}} is the transmembrane ionic current per unit area, C m {\displaystyle C_{m}} is the membrane capacitance per unit area, λ {\displaystyle \lambda } is the intra- to extracellular conductivity ratio, and χ {\displaystyle \chi } is the membrane surface area per unit volume (of tissue).1

Derivation

The monodomain model can be easily derived from the bidomain model. This last one can be written as1 ( Σ i v ) + ( Σ i v e ) = χ ( C m v t + I ion ) ( Σ i v ) + ( ( Σ i + Σ e ) v e ) = 0 {\displaystyle {\begin{aligned}\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v\right)+\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v_{e}\right)&=\chi \left(C_{m}{\frac {\partial v}{\partial t}}+I_{\text{ion}}\right)\\\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v\right)+\nabla \cdot \left(\left(\mathbf {\Sigma } _{i}+\mathbf {\Sigma } _{e}\right)\nabla v_{e}\right)&=0\end{aligned}}}

Assuming equal anisotropy ratios, i.e. Σ e = λ Σ i {\displaystyle \mathbf {\Sigma } _{e}=\lambda \mathbf {\Sigma } _{i}} , the second equation can be written as1 ( Σ i v e ) = λ 1 + λ ( Σ i v ) . {\displaystyle \nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v_{e}\right)=-{\frac {\lambda }{1+\lambda }}\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v\right).}

Then, inserting this into the first bidomain equation gives the unique equation of the monodomain model1 1 1 + λ ( Σ i v ) = χ ( C m v t + I ion ) . {\displaystyle {\frac {1}{1+\lambda }}\nabla \cdot \left(\mathbf {\Sigma } _{i}\nabla v\right)=\chi \left(C_{m}{\frac {\partial v}{\partial t}}+I_{\text{ion}}\right).}

Boundary conditions

Differently from the bidomain model, the monodomain model is usually equipped with an isolated boundary condition, which means that it is assumed that there is not current that can flow from or to the domain (usually the heart).34 Mathematically, this is done imposing a zero transmembrane potential flux (homogeneous Neumann boundary condition), i.e.:4

( Σ i v ) n = 0 on  T × ( 0 , T ) {\displaystyle (\mathbf {\Sigma } _{i}\nabla v)\cdot \mathbf {n} =0\quad \quad {\text{on }}\partial \mathbb {T} \times (0,T)}

where n {\displaystyle \mathbf {n} } is the unit outward normal of the domain and T {\displaystyle \partial \mathbb {T} } is the domain boundary.

See also

See also

References

References

  1. Pullan, Andrew J.; Buist, Martin L.; Cheng, Leo K. (2005). Mathematically modelling the electrical activity of the heart : from cell to body surface and back again. World Scientific. ISBN 978-9812563736.
  2. Keener J, Sneyd J (2009). Mathematical Physiology II: Systems Physiology (2nd ed.). Springer. ISBN 978-0-387-79387-0.
  3. Rossi, Simone; Griffith, Boyce E. (1 September 2017). "Incorporating inductances in tissue-scale models of cardiac electrophysiology". Chaos: An Interdisciplinary Journal of Nonlinear Science. 27 (9): 093926. doi:10.1063/1.5000706. ISSN 1054-1500. PMC 5585078. PMID 28964127.
  4. Boulakia, Muriel; Cazeau, Serge; Fernández, Miguel A.; Gerbeau, Jean-Frédéric; Zemzemi, Nejib (24 December 2009). "Mathematical Modeling of Electrocardiograms: A Numerical Study" (PDF). Annals of Biomedical Engineering. 38 (3): 1071–1097. doi:10.1007/s10439-009-9873-0. PMID 20033779. S2CID 10114284.