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Orthonormal-Basis Partitioning And Time-Frequency Representation of Non-Stationary Signals

Aysin, Benhur (2002) Orthonormal-Basis Partitioning And Time-Frequency Representation of Non-Stationary Signals. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Spectral analysis is important in many fields, such as speech, radar and biomedicine. Many signals encountered in these areas possess time-varying spectral characteristics. The power spectrum indicates what frequencies exist in the signal but it does not show when those frequencies occur. Time-frequency analysisprovides this missing information. A time-frequency representation of the signal shows the intensities of the frequencies in the signal at the times they occur, and thus reveals if and how the frequencies of a signal are changing over time.Time-dependent spectral analysis of beat-to-beat variations of cardiac rhythm, or heart rate variability (HRV), represents a major challenge due to the structure of the signal. A number oftime-frequency representations have been proposed for the estimation of the time-dependent spectra. However, time-frequency analysis of multicomponent physiological signals such as cardiac rhythm is complicated by the presence of numerous, ill-structured frequency elements. We sought to develop a simple method for 1)detecting changes in the structure of the HRV signal, 2)segmenting the signal into pseudo-stationary portions, and 3)exposing characteristic patterns of the changes in thetime-frequency plane. The method, referred to as Orthonormal-Basis Partitioning and Time-Frequency Representation (OPTR), is validated on simulated signals and HRV data. Unlike the traditional time-frequency HRV representations, which are usuallyapplied to short segments of signals recorded in controlled conditions, OPTR can be applied to long and "content-rich" ambulatory signals to obtain the signal representation along withits time-varying spectrum. Thus, the proposed approach extends the scope of applications of the time-frequency analysis to all types of HRV signals and to other physiological data.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChaparro, Luis Fchaparro@engr.pitt.eduLFCH
Committee CoChairShusterman,
Committee MemberEl-Jaroudi, Amroamro@ee.pitt.eduAMRO
Committee MemberLi, Ching-Chungccl@engr.pitt.eduCCL
Committee MemberHebert, Delma
Committee MemberBoston, J. Robertboston@engr.pitt.eduBBN
Date: 20 December 2002
Date Type: Completion
Defense Date: 26 November 2002
Approval Date: 20 December 2002
Submission Date: 2 December 2002
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: eigenvector; feature extraction; spectral analysis; tilt
Other ID:, etd-12022002-131851
Date Deposited: 10 Nov 2011 20:07
Last Modified: 15 Nov 2016 13:52


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