Date of Award
Summer 2011
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical & Computer Engineering
Committee Director
Frederic D. McKenzie
Committee Member
Mohammed Ferdjallah
Committee Member
Ravindra Joshi
Committee Member
Roland R. Mielke
Abstract
An Electromyogram (EMG) is an electrical signal, which is measured from a skeletal muscle during voluntary and involuntary contractions. EMGs are useful in interpreting pathological states of the musculoskeletal system. In particular, EMGs offer valuable information concerning the timing of muscular activity and its relative intensity. Various EMG models have developed with many different purposes from a pure mathematical model to a pattern structure model [17,46]. Sophisticated EMG models are necessary to examine the effects of small changes in muscular morphology and activities [46]. Due to the crucial importance of EMG models, all factors in the model should be precise and accurate. Especially, an intracellular action potential (IAP) model, the starting point of an EMG model, should be precisely generated because of its importance as the main component for an EMG model. Generally, the Rosenfalck IAP model [75,89] has been used because of its computational simplicity [59,72,77]. However, the Rosenfalck IAP model oversimplifies a real IAP, which has been experimentally measured, and it results in mismatching amplitudes and time duration between a real and modeled IAP.
This research proposes a mathematical IAP model using a series of modified gamma and erlang probability density functions. The optimization of the proposed IAP model was conducted by several different numerical methods, namely Gauss-Newton, Steepest Descent, and Conjugate Gradient methods. These optimizing methods for the proposed muscle IAP model were validated by applying them to the experimental results of the Hudgkin and Huxley neuron action potential [11]. Due to the similarity in the mechanism of both nerve and muscle IAP generations, the validation shows that the methods and results are reasonably applied and obtained in the proposed muscle model, which for the first time incorporates properties that explain ion channel behavior in IAP generation.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DOI
10.25777/5gka-wf05
ISBN
9781124990712
Recommended Citation
Kim, GyuTae.
"Statistical Optimizations of Muscle Action Potentials Based on Modeling and Analysis of Ion Channel Dynamics"
(2011). Doctor of Philosophy (PhD), Dissertation, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/5gka-wf05
https://digitalcommons.odu.edu/ece_etds/91
Included in
Biomedical Engineering and Bioengineering Commons, Electrical and Computer Engineering Commons