Signed visibility graphs of time series and their application to brain networks
department or school: Arts and Sciences, Irving K. Barber School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Soni, Gunjan
issued: 2019-04-29T15:46:02Z
issued: 2019
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Time series have been extensively studied and used in many fields to describe time-dependent observations such as rainfall levels, stock market, and even our brain signals. In this thesis, we extend previous network based approach to the analysis of time series, by introducing signs to the edges that describe the visibility graph. The resulting signed visibility graph contains more information about the properties of time series than standard visibility graphs. We designed a dynamic programming algorithm that creates optimal, non-overlapping and time-respecting clusters/partitions in signed visibility graphs. Our method differs from existing approaches of standard network partitioning or community detection methods that do not take into consideration the time-order in a visibility graph. Our signed visibility graph approach can also be used to define mutual information based measure for comparing the correlation between two time series. By taking into consideration the information of a node due to both positive and negative edges in the graphs, our measure is more accurate than the existing correlation measure. As an application, we applied our framework of signed visibility graph to brain networks and studied the clustering of brain time series for various sets of patients suffering from ADHD, Schizophrenia, Bipolar Disorder, and Epilepsy (with & without seizures), compared with normal human brain signals. For fMRI data, these comparisons are done on various levels: starting from individual Regions of Interest (ROIs) of the brain, to groups of ROIs, and lastly to Resting State Networks (RSNs; brain sub-networks/regions). These comparisons gave some interesting results showing differences in various metrics of brain network. For instance, differences were found in the average number of clusters, cluster size, average mutual information measure, average jaccard index values of clusters, with extreme values in certain sub-networks and in certain disease-sets, etc.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/69978
note: Signed Visibility Graphs of TimeSeries and their application to BrainNetworksbyGunjan SoniB.Tech., Mody University of Science and Technology, India, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2019c Gunjan Soni 2019The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Signed Visibility Graphs of Time Series and their application to BrainNetworkssubmitted by Gunjan Soni in partial fulfilment of the requirements of thedegree of Master of Science.Dr. Yong Gao, Irving K. Barber School of Arts & SciencesSupervisorDr. Heinz Bauschke, Irving K. Barber School of Arts & SciencesSupervisory Committee MemberDr. Bowen Hui, Irving K. Barber School of Arts & SciencesSupervisory Committee MemberDr. Ying Zhu, Faculty of ManagementUniversity ExamineriiAbstractTime series have been extensively studied and used in many fields to describetime-dependent observations such as rainfall levels, stock market, and evenour brain signals. In this thesis, we extend previous network based ap-proach to the analysis of time series, by introducing signs to the edges thatdescribe the visibility graph. The resulting signed visibility graph containsmore information about the properties of time series than standard visibilitygraphs.We designed a dynamic programming algorithm that creates optimal,non-overlapping and time-respecting clusters/partitions in signed visibilitygraphs. Our method diâµers from existing approaches of standard networkpartitioning or community detection methods that do not take into con-sideration the time-order in a visibility graph. Our signed visibility graphapproach can also be used to define mutual information based measure forcomparing the correlation between two time series. By taking into consid-eration the information of a n…
has type: Thesis/Dissertation
date issued: 2019-05
is shown at: 10.14288/1.0378503
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/69978