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Proceedings - 10th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2023202310th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2023; East Sarajevo; Bosnia and Herzegovina; 5 June 2023 through 8 June 2023; Category numberCFP23UWD-ART; Code 191225

Data-driven and Physics-informed Muscle Model Surrogates for Cardiac Cycle Simulations(Conference Paper)

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  • aUniversity of Kragujevac, Faculty of Engineering, Sestre Janjic 6, Kragujevac, 3400, Serbia
  • bBioengineering Research and Development Center (BioIRC), Prvoslava Stojanovica 6, Kragujevac, 34000, Serbia
  • cUniversity of Kragujevac, Faculty of Science, Radoja Domanovica 12, Kragujevac, 3400, Serbia
  • dBelgrade Metropolitan University, Tadeusa Koscuska 63, Belgrade, 11000, Serbia
  • eInstitute for Information Technologies, University of Kragujevac, Jovana Cvijica BB, Kragujevac, 34000, Serbia
  • fThe Methodist Hospital Research Institute, The Department of Nanomedicine, Houston, TX 77030, United States
  • gThe Serbian Academy of Sciences and Arts, Kneza Mihaila 35, Belgrade, 11000, Serbia
  • hSerbia and Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovica 6, Kragujevac, 34000, Serbia

Abstract

Health professionals can utilize biomechanical simulations of left ventricle to assess different possible situations and hypothetical scenarios. Understanding of the molecular mechanisms behind muscle contraction has resulted in the development of Huxley-like muscle models. Unlike Hill-type muscle models, Huxley-type muscle models can be used to simulate non-uniform and unstable contractions. However, Huxley models demand considerably more computational resources than Hill models, which limits their practical use in large-scale simulations. To address this, we have developed a data-driven and physics-informed surrogate models that mimic the Huxley muscle model, while requiring significantly less processing power. We collected data from various numerical simulations and trained deep neural networks to replace Huxley's muscle model. Data-driven surrogate model was an order of magnitude faster than the original model, while being quite accurate. Our surrogate models were integrated into a finite element solver and used to simulate a complete cardiac cycle, which would be much harder to do with original Huxley's model. © 2023 IEEE.

Author keywords

finite element analysisHuxley's muscle modelphysics-informed neural networksrecurrent neural networkssurrogate modeling

Indexed keywords

Engineering controlled terms:Deep neural networksMuscleRecurrent neural networks
Engineering uncontrolled termsCardiac cyclesCycle simulationData drivenFinite element analyseHealth professionalsHuxley muscle modelMuscle modelsNeural-networksPhysic-informed neural networkSurrogate modeling
Engineering main heading:Finite element method

Funding details

Funding sponsor Funding number Acronym
451-03-68/2022-14/200378
Horizon 2020 Framework Programme
See opportunities by H2020		777204,952603H2020
Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja451-03-68/2022-14/200107MPNTR

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    ACKNOWLEDGMENT This research was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952603 (http://sgabu.eu/). This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains. Research was also supported by the SILICOFCM project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777204. This article reflects only the authors’ views. The European Commission is not responsible for any use that may be made of the information the article contains. The research was also funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, contract numbers [451-03-68/2022-14/200107 (Faculty of Engineering, University of Kragujevac) and 451-03-68/2022-14/200378 (Institute for Information Technologies Kragujevac, University of Kragujevac)].

  • ISBN: 979-835030711-5
  • Source Type: Conference Proceeding
  • Original language: English
  • DOI: 10.1109/IcETRAN59631.2023.10192144
  • Document Type: Conference Paper
  • Publisher: Institute of Electrical and Electronics Engineers Inc.

  Milicevic, B.; University of Kragujevac, Faculty of Engineering, Sestre Janjic 6, Kragujevac, Serbia;
© Copyright 2023 Elsevier B.V., All rights reserved.

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