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dc.contributor.authorLopez Marcano, Juan L.en
dc.date.accessioned2016-12-13T09:00:13Zen
dc.date.available2016-12-13T09:00:13Zen
dc.date.issued2016-12-12en
dc.identifier.othervt_gsexam:9180en
dc.identifier.urihttp://hdl.handle.net/10919/73688en
dc.description.abstractAs of 2016, diagnosis of ADHD in the US is controversial. Diagnosis of ADHD is based on subjective observations, and treatment is usually done through stimulants, which can have negative side-effects in the long term. Evidence shows that the probability of diagnosing a child with ADHD not only depends on the observations of parents, teachers, and behavioral scientists, but also on state-level special education policies. In light of these facts, unbiased, quantitative methods are needed for the diagnosis of ADHD. This problem has been tackled since the 1990s, and has resulted in methods that have not made it past the research stage and methods for which claimed performance could not be reproduced. This work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. More specifically, the K-nearest Neighbor algorithm and Gaussian-Mixture-Model-based Universal Background Models (GMM-UBM), along with autoregressive (AR) model features, are investigated and evaluated for the classification problem at hand. In this effort, classical KNN and GMM-UBM were also modified in order to account for uncertainty in diagnoses. Some of the major findings reported in this work include classification performance as high, if not higher, than those of the highest performing algorithms found in the literature. One of the major findings reported here is that activities that require attention help the discrimination of ADHD and Non-ADHD subjects. Mixing in EEG data from periods of rest or during eyes closed leads to loss of classification performance, to the point of approximating guessing when only resting EEG data is used.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEEGen
dc.subjectADHDen
dc.subjectClassificationen
dc.subjectMachine Learningen
dc.subjectKNNen
dc.subjectSVMen
dc.subjectGMMen
dc.subjectAutoregressive Coefficientsen
dc.titleClassification of ADHD and non-ADHD Using AR Models and Machine Learning Algorithmsen
dc.typeThesisen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.contributor.committeechairBeex, Aloysius A.en
dc.contributor.committeememberBailey, Scott M.en
dc.contributor.committeememberPaul, JoAnn Maryen


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