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首页> 外文期刊>IEEE Transactions on Biomedical Engineering >Identification of transient renal autoregulatory mechanisms using time-frequency spectral techniques
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Identification of transient renal autoregulatory mechanisms using time-frequency spectral techniques

机译:使用时频频谱技术识别短暂性肾脏自我调节机制

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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.
机译:由于时间肾小管肾小球反馈(TGF)和肌源性(MYO)机制相互作用并共享一个共同的效应子,即传入小动脉,因此很难从时间序列数据中确定肾脏自调节的两个主要介体。而且,尽管这两种机制都可以在充分表征的频带中表现出振荡,但是这些系统通常以无法通过频域分析检测到的非振荡状态工作。为了克服这些困难,我们开发了一种表征TGF和MYO系统的新方法。激光多普勒探头用于测量局部皮层血流(CBF)的波动,以响应血压(BP)的自发变化和对BP的强加扰动,从而引起强烈,同时,短暂,振荡的血流响应。这些瞬态响应通过时间序列数据的高分辨率时频频谱分析来识别。在本报告中,我们比较了四种不同的时频频谱技术(短时傅立叶变换(STFT),平滑伪Wigner-Ville和两种最新开发的方法:希尔伯特-黄变换和时变最优参数搜索(TVOPS) ),以确定这四种方法中哪一种最适合识别肾脏自动调节机制中的瞬时振荡。我们发现TVOPS在仿真示例和实际数据中两个自动调节机制的识别方面始终提供最佳性能。与其他三种方法相比,STFT在时间和频率分辨率上都受到影响,但它能够识别出两种自动调节机制。综上所述,我们的经验表明,可以采用两级方法来分析肾血流(RBF)数据:STFT获得低分辨率的时频频谱图,然后使用高分辨率技术(例如TVOPS)进行分析。需要更高的时频响应瞬态响应分辨率。

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