Abstract
Developing a computational model to model cardiac activity has been increasingly important in recent decades. Accurate cell-level active tension modeling for cardiomyocytes is critical to understanding cardiac functionality on a patient-specific basis and developing an effective in-silico cardiac model. However, cell-level models in the literature fail to account for viscoelasticity and inter-patient variations in active tension. This research proposes a genetic algorithm-optimized, fractional order system to model cell-level active tension by extending Land’s state-of-the-art model of cardiac contraction. The model features the (left) Caputo derivative of six state variables that identify the mechanistic origins of viscoelasticity in a myocardial cell in terms of the thin filament, thick filament, and length-dependent interactions. This proposed Caputo Land System (CLS) model is the first of its kind for active tension modeling in cells and demonstrates notable patient-specificity, with smaller mean square errors than the reference model relative to cell-level experiments, promising greater clinical relevance than its counterparts in the literature.
School
School of Sciences and Engineering
Department
Robotics, Control & Smart Systems Program
Degree Name
MS in Robotics, Control and Smart Systems
Graduation Date
Fall 1-31-2024
Submission Date
7-24-2023
First Advisor
Khalil Elkhodary
Committee Member 1
Anwar Abd Elnaser
Committee Member 2
Ahmed Saleh
Committee Member 3
Mohamed El-Morsi
Extent
75 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Not necessary for this item
Recommended Citation
APA Citation
Elhamshari, A.
(2024).Cardiac Active Tension Modeling via Genetic Algorithm-Optimized Fractional Order Systems [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2173
MLA Citation
Elhamshari, Afnan Khaled. Cardiac Active Tension Modeling via Genetic Algorithm-Optimized Fractional Order Systems. 2024. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2173