Abstract
Heart disease is the leading cause of mortality worldwide. Many cardiac diseases are associated with altered elastic energy relations of the heart tissue. However, the strain energy functions describing these characteristics are limited since they were designed manually for highly specific experimental setups. In this study, we develop CHESRA (Cardiac Hyperelastic Evolutionary Symbolic Regression Algorithm), an automated constitutive modelling framework, to derive cardiac elastic strain energy functions directly from experimental data. Our results indicate that CHESRA finds functions that reproduce mechanical tissue properties from experimental data whilst controlling function complexity. Our novel approach has the potential to find strain-energy functions that fit to various experimental data sets and may contribute to automatically building mathematical models to understand clinical observations of heart diseases.
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Ludwicki, K., Riebel, L.L., Ohnemus, S., Westby, F.M.E., Forsch, N., Balaban, G. (2023). An Automated Cardiac Constitutive Modelling Framework with Evolutionary Strain Energy Functions. In: McCabe, K.J. (eds) Computational Physiology. Simula SpringerBriefs on Computing(), vol 13. Springer, Cham. https://doi.org/10.1007/978-3-031-25374-4_1
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DOI: https://doi.org/10.1007/978-3-031-25374-4_1
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Publisher Name: Springer, Cham
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