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Modeling classification network of electroencephalographic artifacts and signals associated with deep brain stimulation

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TitleInfo
Title
Modeling classification network of electroencephalographic artifacts and signals associated with deep brain stimulation
Name (type = personal)
NamePart (type = family)
Tong
NamePart (type = given)
Saichiu Nelson
NamePart (type = date)
1978-
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Saichiu Nelson Tong
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RoleTerm (authority = RULIB)
author
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Pham
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Hoang
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Hoang Pham
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Advisory Committee
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chair
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Albin
NamePart (type = given)
Susan L.
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Susan L. Albin
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Honggang
DisplayForm
Honggang Wang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Wong
NamePart (type = given)
Stephen
DisplayForm
Stephen Wong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
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2017-01
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2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In the last decade, the study of network has emerged as a useful tool to investigate complex systems with interdependent components because a new set of theoretical tools and practical techniques have been developed to analyze data that can be modeled as networks. The objective of this study is to develop a new reliable framework for classifying network data evolved over time. In particular, this research project conducts a study on EEG data as a form of multivariate time series modeled as networks during a period of time in which a treatment procedure called deep brain stimulation (DBS) is either set to be on or off. DBS is a procedure used to treat symptoms of neurological movement and neuropsychiatric disorders—most commonly for patients with Parkinson’s disease (PD) who do not respond well to medication. The procedure involves the use of a surgically implanted, battery operated neurostimulator to send electrical stimulation to targeted areas in the brain. Currently, conventional DBS therapy is not suitable to be individualized: DBS is only able to deliver stimulation continuously and the DBS setting, such as amount of stimulation delivered, is adjusted based on a trial-and-error process involving subjective clinical assessment. If DBS is able to deliver an adequate amount of stimulation only when it is necessary for an individual patient based on a quantitative metric, adjusting DBS setting will become a more objective process and the therapy will become more effective with less side effect. For such an adaptive DBS to be realized, a detectable feedback signal must be identified that can be used to adjust DBS setting in a feedback control system. Currently, there is lack of such definitive signals that are thought to be reliable as the basis for feedback. Electroencephalographic (EEG) recording is a clinical and electrophysiological monitoring tool used by physicians to diagnose and monitor patients with neurological conditions. EEG measures regional electrical activity of the brain of an individual in the form of a multivariate time series dataset. In this study, a classification framework is developed to identify whether or not a set of EEG signals and its frequency components can be used to develop classifier for identifying EEG signals belonging to a state when the DBS is on vs. a state when the DBS is off. In particular, the framework applies binary classifiers to features derived from networks constructed based on the EEG data. The first part of this study attempts to apply the classification framework by focusing on physiological frequency ranges with three different binary classifiers including logistic regression, least absolute shrinkage and selection operator (LASSO), and a new method known as principal component stepwise selection logistic regression (PCSSLR) that is developed in this thesis. The second part attempts to identify potential biomarkers to be used as feedback control signals for an adaptive DBS device. The results of both parts will help to address the question of whether or not EEG data can be used to detect feedback signals for use in adaptive DBS feedback control system and to establish a modeling framework to develop similar classifier for performing statistical classification based on network features to identify which group a temporal signal belongs to.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = ETD-LCSH)
Topic
Diagnostic imaging
Subject (authority = ETD-LCSH)
Topic
Brain stimulation
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_7821
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xvi, 236 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Saichiu Nelson Tong
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3J67KCR
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tong
GivenName
Saichiu
MiddleName
Nelson
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-01-04 12:36:55
AssociatedEntity
Name
Saichiu Tong
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-01-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after January 31st, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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