Di Virgilio V, Barbieri R, Mainardi L, Strano S, Cerutti S
Department of Information and System Sciences, University La Sapienza, Rome, Italy.
Med Eng Phys. 1997 Mar;19(2):109-24. doi: 10.1016/s1350-4533(96)00058-6.
This paper approaches the problem of short-term mechanisms that regulate heart rate and blood pressure variability signals, by focusing the evident changes of their frequency content during transients (dynamic situations in which the behaviour of these control mechanisms may vary on a beat-to-beat basis). In this study, we suggest an autoregressive time-variant spectral estimation method, which is able to follow such dynamic changes in the signals. This method has also been extended to a multivariate approach in order to take into account more than one process at a time, and to assess the mutual influences between the different controlling systems. The algorithms successfully tested on simulated series have also been used to analyse series recorded during a vaso-vagal syncope episode in a tilt manoeuvre and a physical exercise stress test protocol. The results show how this method is able to follow the changing dynamics of the signals on the basis of a closed-loop model of their interaction on a beat-to-beat basis. After a proper identification procedure of the blocks forming the model, it is possible, therefore, to obtain the classical spectral parameters and the gain of the transfer function between the signals. Such parameters constitute new time series that describe the physiopathology of the cardiovascular control systems, even during non-stationary epochs.
本文通过关注心率和血压变异性信号在瞬态过程（这些控制机制的行为可能逐搏变化的动态情况）中频率成分的明显变化，探讨调节这些信号的短期机制问题。在本研究中，我们提出了一种自回归时变谱估计方法，该方法能够跟踪信号中的此类动态变化。此方法还扩展为多变量方法，以便同时考虑多个过程，并评估不同控制系统之间的相互影响。在模拟序列上成功测试的算法也已用于分析在倾斜试验中的血管迷走性晕厥发作和体育锻炼压力测试协议期间记录的序列。结果表明，该方法能够基于信号逐搏相互作用的闭环模型跟踪信号变化的动态。因此，在对构成模型的模块进行适当的识别过程之后，有可能获得经典的谱参数以及信号之间传递函数的增益。即使在非平稳时期，这些参数也构成描述心血管控制系统生理病理学的新时间序列。
