ATTENTION: The software behind KU ScholarWorks is being upgraded to a new version. Starting July 15th, users will not be able to log in to the system, add items, nor make any changes until the new version is in place at the end of July. Searching for articles and opening files will continue to work while the system is being updated.
If you have any questions, please contact Marianne Reed at mreed@ku.edu .
The Application of Detrended Fluctuation Analysis and Adaptive Fractal Analysis on Center of Pressure Time Series in Parkinson's Disease
dc.contributor.advisor | Luchies, Carl | |
dc.contributor.author | Weilert, Melanie | |
dc.date.accessioned | 2018-02-01T02:47:36Z | |
dc.date.available | 2018-02-01T02:47:36Z | |
dc.date.issued | 2017-05-31 | |
dc.date.submitted | 2017 | |
dc.identifier.other | http://dissertations.umi.com/ku:15148 | |
dc.identifier.uri | http://hdl.handle.net/1808/25868 | |
dc.description.abstract | The long term goal of this thesis is to create quantitative, clinically significant measures that allow for early detection of Parkinson’s disease (PD) postural instability (PI), the progression of PI due to PD progression, and ultimately, fall risk in PD patients. Current clinical assessments in PD are not sufficiently sensitive to predict fall risk. Although biomechanical postural sway measures have provided quantitative characterization towards the progression of PI associated with PD progression, these methods are still not sufficiently sensitive to allow for early detection of PD and fall risk. Thus, a need arises for new quantitative methods to be established which can further describe PI progression in PD. This thesis had two overall goals: • Evaluate the appropriate selection of input parameters of detrended fluctuation analysis (DFA) and adaptive fractal analysis (AFA) in simulated signals. • Test the sensitivity of AFA, as compared to DFA, towards center of pressure velocity (COPv) time series towards characterization of postural instability (PI) progression in patients with Parkinson’s disease. Specific Aim 1 determined through iterative testing of input parameter combinations that both AFA and DFA are highly sensitive to input parameters when considering fractional Brownian motion (fBm) signals. Input parameter ranges for fBm-like signals in appropriately-large biological data should be examined at maximum window sizes (nmax) values between N/6 and N/10; minimum window sizes (nmin) values around 4 to 6 samples; and for fitted polynomial order (M) for AFA to remain first order. Specific Aim 2 showed that fractal analysis methods may be sensitive towards detecting the development and progression of PI in PD. AFA and DFA were tested on postural sway data collected in a previous study that used mild PD patients (Hoehn and Yahr stage (H&Y) 2, without postural deficits), moderate PD patients (H&Y 3, with postural deficits), and age-matched healthy controls (HC). AFA produced the most clinically significant measure, Hfast, which detected changes in COPv dynamics across smaller time scales than other parameters. These results suggest that components of fractal analysis on COPv time series could be used in concert with traditional quantitative and clinical measures to further enhance the sensitivity of clinical analysis, the understanding of PD PI dynamics and progression, and development of predictive computational simulations of motor and postural control in PD. | |
dc.format.extent | 199 pages | |
dc.language.iso | en | |
dc.publisher | University of Kansas | |
dc.rights | Copyright held by the author. | |
dc.subject | Biomechanics | |
dc.subject | AFA | |
dc.subject | DFA | |
dc.subject | fractal analysis | |
dc.subject | motor control | |
dc.subject | nonlinear analysis | |
dc.subject | Parkinson's disease | |
dc.title | The Application of Detrended Fluctuation Analysis and Adaptive Fractal Analysis on Center of Pressure Time Series in Parkinson's Disease | |
dc.type | Thesis | |
dc.contributor.cmtemember | Shontz, Suzanne | |
dc.contributor.cmtemember | Fang, Huazhen | |
dc.thesis.degreeDiscipline | Bioengineering | |
dc.thesis.degreeLevel | M.S. | |
dc.identifier.orcid | ||
dc.rights.accessrights | openAccess |
Files in this item
This item appears in the following Collection(s)
-
Engineering Dissertations and Theses [1055]
-
Theses [4088]