Zou Rui, Chon Ki H
Department of Neurosurgery, Children's Hospital, Boston, MA 02115, USA.
IEEE Trans Biomed Eng. 2004 Feb;51(2):219-28. doi: 10.1109/TBME.2003.820381.
We introduce a new method to estimate reliable time-varying (TV) transfer functions (TFs) and TV impulse response functions. The method is based on TV autoregressive moving average models in which the TV parameters are accurately obtained using the optimal parameter search method which we have previously developed. The new method is more accurate than the recursive least-squares (RLS), and remains robust even in the case of significant noise contamination. Furthermore, the new method is able to track dynamics that change abruptly, which is certainly a deficiency of the RLS. Application of the new method to renal blood pressure and flow revealed that hypertensive rats undergo more complex and TV autoregulation in maintaining stable blood flow than do normotensive rats. This observation has not been previously revealed using time-invariant TF analyses. The newly developed approach may promote the broader use of TV system identification in studies of physiological systems and makes linear and nonlinear TV modeling possible in certain cases previously thought intractable.
我们介绍了一种估计可靠的时变（TV）传递函数（TFs）和TV脉冲响应函数的新方法。该方法基于TV自回归移动平均模型，其中使用我们之前开发的最优参数搜索方法准确获取TV参数。新方法比递归最小二乘法（RLS）更精确，即使在存在显著噪声污染的情况下也能保持稳健。此外，新方法能够跟踪突然变化的动态，这无疑是RLS的一个缺陷。将新方法应用于肾血压和血流显示，与正常血压大鼠相比，高血压大鼠在维持稳定血流方面经历更复杂的TV自动调节。使用时不变TF分析以前尚未揭示这一观察结果。新开发的方法可能会促进TV系统识别在生理系统研究中的更广泛应用，并使在某些以前认为难以处理的情况下进行线性和非线性TV建模成为可能。
