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An Assessment of Fractal Characterization Methods for 1/f Processes with Application to the Analysis of Stride Interval Time Series

Schaefer, Alexander (2013) An Assessment of Fractal Characterization Methods for 1/f Processes with Application to the Analysis of Stride Interval Time Series. Master's Thesis, University of Pittsburgh. (Unpublished)

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The time evolution and complex interactions of many nonlinear systems, such as in the
human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/f. The scaling exponent can be interpreted as the degree of fractal characteristic and thus as a "biomarker" of relative health and decline. This thesis presents a thorough numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index. In consideration of the constraints in applications, the significant drawbacks of proposed time domain methods are noted, and it is concluded that time-scale domain wavelet methods can provide a reasonably consistent and accurate biomarker technique for these fractal time series.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSejdic, Ervinesejdic@pitt.eduESEJDIC
Committee MemberLevitan, Stevenlevitan@pitt.eduLEVITAN
Committee MemberEl-Jaroudi, Amroamro@pitt.eduAMRO
Date: 24 September 2013
Date Type: Publication
Defense Date: 24 July 2013
Approval Date: 24 September 2013
Submission Date: 24 July 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 65
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: fractals, time series analysis, self similarity, gait, stride intervals, DFA, wavelets, 1/f process
Date Deposited: 24 Sep 2013 20:01
Last Modified: 15 Nov 2016 14:14


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