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http://hdl.handle.net/11375/24797
Title: | Stepping Beyond Behaviour: Explainable Machine Learning for Clinical Neurophysiological Assessment of Concussion Progression |
Authors: | Boshra, Rober |
Advisor: | Dr. John F. Connolly, Dr. James P. Reilly |
Department: | Biomedical Engineering |
Keywords: | Concusssion;Mild Traumatic Brain Injury;Machine Learning;Explainable Machine Learning;Deep Learning;Injury Progression;Clinical Tools;Functional Connectivity;Electroencephalography;Event-Related Potentials |
Publication Date: | 2019 |
Abstract: | The present dissertation details a sequence of studies in mild traumatic brain injury, the progression of its effects on the human brain as recorded by event-related electroencephalography, and potential applications of machine learning algorithms in detecting such effects. The work investigated data collected from two populations (in addition to healthy controls): 1) a recently-concussed adolescent group, and 2) a group of retired football athletes who sustained head trauma a number of years prior to testing. Neurophysiological effects of concussion were assessed across both groups with the same experimental design using a multi-deviant auditory oddball paradigm designed to elicit the P300 and other earlier components. Explainable machine learning models were trained to classify concussed individuals from healthy controls. Cross-validation performance accuracies on the recently-concussed (chapter 4) and retired athletes (chapter 3) were 80% and 85%, respectively. Features showed to be most useful in the two studies were different, motivating a study of potential differences between the different injury-stage/age groups (chapter 5). Results showed event-related functional connectivity to modulate differentially between the two groups compared to healthy controls. Leveraging results from the presented work a theoretical model of mild traumatic brain injury progression was proposed to form a framework for synthesizing hypotheses in future research. |
URI: | http://hdl.handle.net/11375/24797 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Boshra_Rober_2019July_PhD.pdf | 6.8 MB | Adobe PDF | View/Open |
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