Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A study of brain networks associated with swallowing via electroencephalography signals

Jestrovic, Iva (2016) A study of brain networks associated with swallowing via electroencephalography signals. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img] PDF
Primary Text
Restricted to University of Pittsburgh users only until 15 June 2021.

Download (13MB) | Request a Copy

Abstract

Swallowing and swallowing disorders have garnered continued interest over the past several decades. While physiological origins of the swallowing activity are well understood, it remains uncertain how this activity affects the central nervous system and brain activity. Electroencephalography (EEG) systems can enable our study of cerebral activation patterns during the performance of swallowing tasks, and can also possibly allow us to make inferences about the nature of abnormal neurological conditions causing swallowing difficulties. EEG also lends itself to techniques for analysis which provide insight into the interactions between brain regions, and enables the measuring and analysis of functional interactions between different brain regions by way of the graph theoretical and signal processing on graph approach. In this dissertation we provided better insight into the neurology of swallowing by focusing our research towards the three main areas of investigation. First, we showed that the EEG signals during the swallowing can be considered as non stationary. These findings provided an answer about choosing an appropriate technique for forming connectivity brain networks. Second, using graph theory and signal processing on graphs we showed that there are differences between swallowing in the neutral and chin-tuck head positions, between swallowing of different stimuli, between normal swallowing and swallowing with a distraction, and between consecutive swallows. Lastly, we improved an algorithm used to calculate the vertex-frequency information from the signals on graph that we used in our investigation. Particularly, we overcame the limitations of the computational complexity and the fixed window size associated with the windowed graph Fourier transform.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Jestrovic, Ivaivj2@pitt.eduIVJ2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSejdić, Ervinesejdic@pitt.eduESEJDIC
Committee MemberZhi-Hong, Maozhm4@pitt.eduZHM4
Committee MemberEl-Jaroudi, Amroamro@pitt.eduAMRO
Committee MemberMcDermott, Thomastem42@pitt.eduTEM42
Committee MemberCoyle, James Ljcoyle@pitt.eduJCOYLE
Date: 15 June 2016
Date Type: Publication
Defense Date: 22 March 2016
Approval Date: 15 June 2016
Submission Date: 26 March 2016
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 162
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Dysphagia, brain network, graph theory, signal processing on graphs
Date Deposited: 15 Jun 2016 18:19
Last Modified: 15 Nov 2016 14:32
URI: http://d-scholarship.pitt.edu/id/eprint/27352

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item