• DocumentCode
    810550
  • Title

    Identification of transient renal autoregulatory mechanisms using time-frequency spectral techniques

  • Author

    Wang, Hengliang ; Siu, Kin ; Ju, Kihwan ; Moore, Leon C. ; Chon, Ki H.

  • Author_Institution
    Dept. of Biomed. Eng., State Univ. of New York, Stony Brook, NY, USA
  • Volume
    52
  • Issue
    6
  • fYear
    2005
  • fDate
    6/1/2005 12:00:00 AM
  • Firstpage
    1033
  • Lastpage
    1039
  • Abstract
    Identification of the two principal mediators of renal autoregulation from time-series data is difficult, as both the tubuloglomerular feedback (TGF) and myogenic (MYO) mechanisms interact and share a common effector, the afferent arteriole. Moreover, although both mechanisms can exhibit oscillations in well-characterized frequency bands, these systems often operate in nonoscillatory states not detectable by frequency-domain analysis. To overcome these difficulties, we have developed a new approach to the characterization of the TGF and MYO systems. A laser Doppler probe is used to measure fluctuations in local cortical blood flow (CBF) in response to spontaneous changes in blood pressure (BP) and to large imposed perturbations in BP, which elicit strong, simultaneous, transient, oscillatory blood flow responses. These transient responses are identified by high-resolution time-frequency spectral analysis of the time-series data. In this report, we compare four different time-frequency spectral techniques (the short-time Fourier transform (STFT), smoothed pseudo Wigner-Ville, and two recently developed methods: the Hilbert-Huang transform and time varying optimal parameter search (TVOPS)) to determine which of these four methods is best suited for the identification of transient oscillations in renal autoregulatory mechanisms. We found that TVOPS consistently provided the best performance in both simulation examples and identification of the two autoregulatory mechanisms in actual data. While the STFT suffers in time and frequency resolution as compared to the other three methods, it was able to identify the two autoregulatory mechanisms. Taken together, our experience suggests a two level approach to the analysis of renal blood flow (RBF) data: STFT to obtain a low-resolution time-frequency spectrogram, followed by the use of a higher resolution technique, such as the TVOPS, if even higher time-frequency resolution of the transient responses is required.
  • Keywords
    Fourier transforms; blood pressure measurement; feedback; kidney; medical signal processing; spectral analysis; time series; Hilbert-Huang transform; afferent arteriole; blood pressure; cortical blood flow; high-resolution time-frequency spectral analysis; laser Doppler probe; myogenic mechanisms; renal autoregulatory mechanisms; renal blood flow; short-time Fourier transform; simultaneous transient oscillatory blood flow; smoothed pseudo Wigner-Ville method; time varying optimal parameter search; time-series data; transient renal autoregulatory mechanisms; tubuloglomerular feedback; Blood flow; Feedback; Fluid flow measurement; Fourier transforms; Frequency domain analysis; Laser transitions; Pressure measurement; Probes; Time frequency analysis; Transient analysis; Hilbert-Huang transform; myogenic; renal blood flow; short-time Fourier transform; smoothed pseudo Wigner-Ville; time-varying optimal parameter search algorithm; tubuloglomerular feedback; Algorithms; Animals; Blood Flow Velocity; Blood Pressure; Computer Simulation; Diagnosis, Computer-Assisted; Hemostasis; Kidney; Kinetics; Laser-Doppler Flowmetry; Male; Models, Biological; Rats; Rats, Sprague-Dawley; Renal Circulation; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2005.846720
  • Filename
    1431077