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New Wiener System based modeling and signal processing method for characterization of vascular function

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Title
New Wiener System based modeling and signal processing method for characterization of vascular function
Name (type = personal)
NamePart (type = family)
Patel
NamePart (type = given)
Amit
NamePart (type = date)
1988-
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Amit Patel
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Li
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John K-J
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John K-J Li
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Advisory Committee
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chair
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NamePart (type = family)
Gajic
NamePart (type = given)
Zoran
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Zoran Gajic
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Shoane
NamePart (type = given)
George K
DisplayForm
George K Shoane
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Drzewiecki
NamePart (type = given)
Gary M
DisplayForm
Gary M Drzewiecki
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
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Central aortic blood pressure waveform (P_a) is a critical determinant of the state of overall cardiovascular function, but it cannot be measured directly by noninvasive means. Numerous attempts were made to derive P_a from noninvasively measured peripheral pressure (P_p) using mathematical transformations, transfer function or arterial system modeling approaches. These techniques, in general, do not account for inter-subject or intra-subject variability. A few methods have recently been proposed to generate personalized adaptive transfer function employing arterial system modeling. However, these personalized models have to be calibrated across different patients at different times and the model algorithms are very sensitive to calibration technique and calibration error. More recently, multi-channel blind system identification (MBSI) have been implemented on these systems to mathematically derive common source P_a based on multiple P_p inputs. This method seems to afford self-calibrating and minimizes estimation error. In general, MBSI approaches are more convenient and practical for aortic pressure estimation, but have not been widely adopted. In this thesis, the arterial system is proposed to be modeled as a Weiner System with linear finite impulse response (FIR) filter accounting for larger arteries transmission channel and non-linear memoryless function block accounting for all nonlinearities due to narrowing of arteries, branching and visco-elastic forces. This model is then experimentally validated with seven human blood pressure datasets. Single input and multiple output (SIMO) or aortic-to-radial arterial transmission channel and aortic-to-femoral arterial transmission channel are established. To model the nonlinear memoryless monotonic function in the Wiener System model a correlation study is performed for linear finite impulse response (FIR) filter simulated peripheral pressure vs. measured peripheral pressure waveform. Each of this correlation curves were fitted to linear, quadratic and cubic polynomial equation. It was found that Wiener model with 3rd order polynomial function yielded better modelling accuracy than that from 2nd order polynomial function which in turn was better than mere linear FIR filter. P_a estimation technique is then presented by modeling arterial system as Multi-channel Weiner System. With this structure when pressure waveforms are measured from two distinct peripheral locations, multichannel blind system identification (MBSI) technique can be used to estimate common input pressure signal or P_a. Nonlinear MBSI method was employed on human blood pressure waveforms (7 datasets). Results show P_a can be accurately derived. This method by nature is self-calibrating to account for any inter-personal, along with intra-personal, vascular dynamics inconstancy. Besides P_a estimation, the proposed MBSI method also allows extraction of system dynamics for vascular channels. Initially, linear finite impulse response (FIR) filter is assumed to be of fixed 10th order in the Wiener System model across all patient dataset. To further improve performance of this aortic pressure estimation method, a new and improved method is developed which estimates channel order preceding arterial system identification. By using effective channel order, system identification is optimized which then enhances aortic pressure estimation. Results showed significant improvement over our earlier method with far more accurate aortic pressure estimation. The outcome of the novel method as presented by this dissertation has the potential to enhance clinical diagnostic accuracy and subsequent treatment efficacy assessment.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8531
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 123 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Aorta
Subject (authority = ETD-LCSH)
Topic
Blood pressure
Note (type = statement of responsibility)
by Amit Patel
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T36113G6
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Patel
GivenName
Amit
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-10-17 10:25:01
AssociatedEntity
Name
Amit Patel
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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