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

被引:14
|
作者
Wang, HL
Siu, K
Ju, K
Moore, LC
Chon, KH
机构
[1] SUNY Stony Brook, Dept Biomed Engn, Stony Brook, NY 11794 USA
[2] SUNY Stony Brook, Dept Physiol & Biophys, Stony Brook, NY 11794 USA
关键词
Hilbert-Huang transform; myogenic; renal blood flow; short-time Fourier transform; smoothed pseudo Wigner-Ville; time-varying optimal parameter search algorithm; tubuloglomerular feedback;
D O I
10.1109/TBME.2005.846720
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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 or 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.
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页码:1033 / 1039
页数:7
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