TY - GEN
T1 - Integration of Surrogate Huxley Muscle Model into Finite Element Solver for Simulation of the Cardiac Cycle
AU - Milicevic, Bogdan
AU - Simic, Vladimir
AU - Milosevic, Miljan
AU - Ivanovic, Milos
AU - Stojanovic, Boban
AU - Kojic, Milos
AU - Filipovic, Nenad
N1 - Funding Information:
Research 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.
Funding Information:
* Research 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’ view. The European Commission is not responsible for any use that may be made of the information the article contains. This work was supported by the Serbian Ministry of Education, Science and Technological Development (Agreement No. 451-03-9/2021-14/200122).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Clinicians can use biomechanical simulations of cardiac functioning to evaluate various real and fictional events. Our present understanding of the molecular processes behind muscle contraction has inspired Huxley-like muscle models. Huxley-type muscle models, unlike Hill-type muscle models, are capable of modeling non-uniform and unstable contractions. Huxley's computational requirements, on the other hand, are substantially higher than those of Hill-type models, making large-scale simulations impractical to use. We created a data-driven surrogate model that acts similarly to the original Huxley muscle model but requires substantially less processing power in order to make the Huxley muscle models easier to use in computer simulations. We gathered data from multiple numerical simulations and trained a deep neural network based on gated-recurrent units. Once we accomplished satisfying precision, we integrated the surrogate model into our finite element solver and simulated a full cardiac cycle. Clinical Relevance - This enables clinicians to track the effects of changes in muscles at the microscale to the cardiac contraction (macroscale).
AB - Clinicians can use biomechanical simulations of cardiac functioning to evaluate various real and fictional events. Our present understanding of the molecular processes behind muscle contraction has inspired Huxley-like muscle models. Huxley-type muscle models, unlike Hill-type muscle models, are capable of modeling non-uniform and unstable contractions. Huxley's computational requirements, on the other hand, are substantially higher than those of Hill-type models, making large-scale simulations impractical to use. We created a data-driven surrogate model that acts similarly to the original Huxley muscle model but requires substantially less processing power in order to make the Huxley muscle models easier to use in computer simulations. We gathered data from multiple numerical simulations and trained a deep neural network based on gated-recurrent units. Once we accomplished satisfying precision, we integrated the surrogate model into our finite element solver and simulated a full cardiac cycle. Clinical Relevance - This enables clinicians to track the effects of changes in muscles at the microscale to the cardiac contraction (macroscale).
UR - http://www.scopus.com/inward/record.url?scp=85138127677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138127677&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9870995
DO - 10.1109/EMBC48229.2022.9870995
M3 - Conference contribution
C2 - 36086276
AN - SCOPUS:85138127677
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3943
EP - 3946
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -