Date of Award
Master of Science
Judith Soberman MD
Atrial fibrillation (AF) is a widely prevalent arrhythmia which affects approximately 4.5 million people in Europe and the US, reducing quality of life and increasing risk of stroke and death. Considerable research effort is currently directed to the arrhythmia because the mechanisms causing its initiation, maintenance, and termination are not well understood. This study addresses, at a tissue level, the basic descriptors of atrial fibrillation, known causes, and a model to utilize in analyzing heart wave characteristics such as propagation velocity, action potential duration and others for potential use in developing early detection methods of AF. The tissue model methodology, based on epicardial tissue and kinetics from a cellular model, utilized the MATLAB PDEPE solver on a 1 X .0011 cm tissue strip, enabling research into complex action potential (AP) and AP propagation characteristics. The model represented left atrial tissue sinus rhythm (SR) and AF states as defined in Grandi et al.23 with comprehensive state variables and ionic currents as well as complete cellular physiology including excitation contraction coupling with sarcoplasmic reticulum Ca2+ ATPase, calcium-induced calcium release, Ca2+ and Na+ buffer fluxes, subcellular sections and diffusion factors, Based on following the nonlinear response cascade of the potassium channels during the progression to AF, the tissue model study provides insight into wave propagation velocity, atrial AP amplitude, action potential duration, non-refractory periods, in addition to other wave characteristics through rates of change, greater then 50%. Regardless of the many known forms of AF, which often involve polygenetic and other cardiac pathologies, this human tissue model study, with its completeness of cardiac cell and tissue representation, provides a basis for further analysis research and modeling because the cardiac structures used can be modified to represent or independently model other potential pathologies that represent early risk warning indicators.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Herringshaw, Michael, "Advancing the Early Detection of Atrial Fibrillation Through In Silico Tissue Modeling" (2015). Electronic Theses and Dissertations. 1179.