Mild Traumatic Brain Injury Assessment using Functional Connectivity Estimators on Resting State EEG Analysis

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dc.contributor.advisorZouridakis, George
dc.contributor.committeeMemberMerchant, Fatima Aziz
dc.contributor.committeeMemberPollonini, Luca
dc.creatorRosas, Alberto
dc.creator.orcid0000-0003-4062-2982
dc.date.accessioned2019-09-19T15:33:03Z
dc.date.available2019-09-19T15:33:03Z
dc.date.createdAugust 2019
dc.date.issued2019-08
dc.date.submittedAugust 2019
dc.date.updated2019-09-19T15:33:05Z
dc.description.abstractIn the US, over 2 million people su er from mild traumatic brain injury (mTBI), however in some cases it goes undiagnosed due to lack of symptoms or non-apparent lesion in conventional imaging. In attempts to create an mTBI biomarker, we analyze brain connectivity based on Phase Locking Values (PLV), imaginary PLV, and amplitude envelope correlation (AEC) computed from electroencephalography activity obtained at the resting state in thirteen mTBI and eight normal control subjects during two visits. The study was set up to examine difference groups within visits and groups between visits to observe any signs of deficits or recovery after injury. Functional connectivity graph theory was computed for both intra- and cross-frequency interactions for each subject individually. Finally, classification of the subjects was performed using Support Vector Machines with linear kernel to rank the features and separate the groups effectively.
dc.description.departmentEngineering Technology, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/4893
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectEEG
dc.subjectMTBI
dc.subjectFunctional Connectivity
dc.subjectPLV
dc.titleMild Traumatic Brain Injury Assessment using Functional Connectivity Estimators on Resting State EEG Analysis
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Technology
thesis.degree.departmentEngineering Technology, Department of
thesis.degree.disciplineEngineering Technology
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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