Kouchakpour H, Allen R, Simpson D M
ISVR, Southampton University, SO17 1BJ, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2375-8. doi: 10.1109/IEMBS.2010.5627266.
Autoregulation refers to the automatic adjustment of blood flow to supply the required oxygen and glucose and remove waste, in proportion to the tissue's requirement at any instant of time. For the brain, cerebral autoregulation is an active process by which cerebral blood flow is controlled at an approximately steady level despite changes in the arterial blood pressure. Robust assessment of the cerebral autoregulation by a model that characterizes this system has been the goal of many studies, searching for techniques that can be used in clinical scenarios to detect potentially dangerous impairment of control. Multiple input, single output (MISO) models can be used to assess autoregulation, and system parameters can be estimated from spontaneous beat-to-beat variations in arterial blood pressure (ABP) and breath-by-breath end-tidal carbon dioxide (P(ETCO2)) as inputs, and cerebral blood flow velocity (CBFV) as the output. In this study a non-linear, multivariate approach, based on Volterra-type kernel estimation models is employed. The results are compared with linear models as well as nonlinear single-input single-output (SISO) models. The normalized mean squared error was used as the criteria of performance of each model in assessing cerebral autoregulation. Our simulation results indicate that for relatively short signals (around 300 sec), nonlinear, multiple-input models based on Volterra systems performed best, though the benefit varied considerably between subjects. When using a fixed model for all recordings, a linear SISO model with ABP as input provided the smallest average modeling error.
自动调节是指血流的自动调整，以供应所需的氧气和葡萄糖并清除废物，其与组织在任何时刻的需求成比例。对于大脑而言，脑自动调节是一个主动过程，通过该过程，尽管动脉血压发生变化，脑血流量仍能维持在大致稳定的水平。许多研究的目标是通过表征该系统的模型对脑自动调节进行可靠评估，寻找可用于临床场景以检测潜在危险的控制功能损害的技术。多输入单输出（MISO）模型可用于评估自动调节，系统参数可根据动脉血压（ABP）的逐搏自发变化和呼气末二氧化碳（P(ETCO2)）的逐次呼吸变化作为输入，以及脑血流速度（CBFV）作为输出进行估计。在本研究中，采用了基于Volterra型核估计模型的非线性多变量方法。将结果与线性模型以及非线性单输入单输出（SISO）模型进行比较。归一化均方误差用作每个模型评估脑自动调节性能的标准。我们的模拟结果表明，对于相对较短的信号（约300秒），基于Volterra系统的非线性多输入模型表现最佳，尽管个体之间的益处差异很大。当对所有记录使用固定模型时，以ABP作为输入的线性SISO模型提供了最小的平均建模误差。
