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Neurophysiological Characterization of a Non-Human Primate Model of Spinal Cord Injury Utilizing Intramuscular Electromyography

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Title: Neurophysiological Characterization of a Non-Human Primate Model of Spinal Cord Injury Utilizing Intramuscular Electromyography
Author: Masood, Farah
Department: School of Engineering
Program: Engineering
Advisor: Hussein, Abdullah
Abstract: The lack of a valid non-human primate (NHP) model of spinal cord injury (SCI) and a robust assessment tool are the key reasons behind the low rate of successful clinical trials for the development of SCI medication. Accordingly, developing such an NHP model would fill the gap between preclinical and clinical studies. A histopathological analysis would help to validate a developed NHP model, while neurophysiological characterizations could offer a reliable assessment tool for the effect of the injury. The goal of this work is to submit a neurophysiological analysis using intramuscular electromyography (EMG) signals collected from the agonist-antagonist pair of tail muscles of Macaca fasicularis during pre- and post-lesion, and for a treatment and control groups. The main goal was achieved by setting three sub-goals to be addressed in this work. First, the general volitional muscle activity was analyzed utilizing some basic amplitude features. Second, an analysis of the motor unit discharge properties was submitted using wavelet transforms (WT) and the relative power (RP) of the multi-resolution EMG signals. Third, the development of an SCI classification system was studied and analyzed by employing various classification approaches, including regular machine learning (ML) and deep learning (DL) classification techniques The findings were consistent with that of the literature. Evidence suggested that the newly proposed general volitional muscle metric (Q-metric) was related directly to the gross recruitment of motor units required for volitional muscle control pre- and post-lesion. As well, the results of the multi-resolution analysis demonstrated the capability of utilizing the WT and the RP as a decomposition method to characterize the discharge properties of the dynamic muscle activity. The findings of the SCI classification analysis implied that the proposed Q-metric and WT-RPs features could be used to build an SCI classification system using K-nearest neighbors (KNN) technique. Finally, the deep learning classification, which consisted exclusively of convolutional neural network (CNN) using raw EMG segments as inputs, showed high potential and promising results for use as an SCI classification system with an F-measure of 84.0% and 86.9%. for the left and the right side.
Date: 2020-01
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
Embargoed Until: 2020-12-19

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Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International