Abstract

Cerebral autoregulation in healthy humans was studied using a novel methodology adapted from Bendat nonlinear analysis technique. A computer simulation of a high-pass filter in parallel with a cubic nonlinearity followed by a low-pass filter was analyzed. A linear system transfer function analysis showed an incorrect estimate of the gain, cut-off frequency, and phase of the high-pass filter. By contrast, using our nonlinear systems identification, yielded the correct gain, cut-off frequency, and phase of the linear system, and accurately quantified the nonlinear system and following low-pass filter. Adding the nonlinear and linear coherence function indicated a complete description of the system. Cerebral blood flow velocity and arterial pressure were measured in six data sets. Application of the linear and nonlinear systems identification techniques to the data showed a high-pass filter, like the linear transfer function, but the gain was smaller. The phase was similar between the two techniques. The linear coherence was low for frequencies below 0.1 Hz but improved by including a nonlinear term. The linear + nonlinear coherence was approximately 0.9 across the frequency bandwidth, indicating an improved description over the linear system analysis of the cerebral autoregulation system.

中文翻译：

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一种用于人类大脑自动调节的新型非线性系统识别：计算机模拟和验证

摘要

使用改编自 Bendat 非线性分析技术的新方法研究了健康人的大脑自动调节。分析了与三次非线性并联的高通滤波器和低通滤波器的计算机模拟。线性系统传递函数分析显示对高通滤波器的增益、截止频率和相位的估计不正确。相比之下，使用我们的非线性系统识别，得到了线性系统的正确增益、截止频率和相位，并准确地量化了非线性系统和跟随低通滤波器。添加非线性和线性相干函数表明系统的完整描述。在六个数据集中测量脑血流速度和动脉压。将线性和非线性系统识别技术应用于数据显示了一个高通滤波器，类似于线性传递函数，但增益较小。两种技术之间的阶段相似。对于低于 0.1 Hz 的频率，线性相干性较低，但通过包含非线性项得到改善。整个频率带宽的线性 + 非线性相干性约为 0.9，表明对脑自动调节系统的线性系统分析的描述有所改进。