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

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 (Unpublished)

[img]
Preview
PDF
Primary Text

Download (2MB) | Preview

Abstract

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.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Aysin, Benhuraysinb@msx.upmc.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChaparro, Luis Fchaparro@engr.pitt.eduLFCH
Committee CoChairShusterman, Vladimirshustermanv@msx.upmc.edu
Committee MemberEl-Jaroudi, Amroamro@ee.pitt.eduAMRO
Committee MemberLi, Ching-Chungccl@engr.pitt.eduCCL
Committee MemberHebert, Delma Jdjh@stargate.pitt.edu
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: http://etd.library.pitt.edu:80/ETD/available/etd-12022002-131851/, etd-12022002-131851
Date Deposited: 10 Nov 2011 20:07
Last Modified: 15 Nov 2016 13:52
URI: http://d-scholarship.pitt.edu/id/eprint/9938

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item