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正常血压和高血压大鼠肾神经峰间隔序列的混沌特征

Chaotic characteristics of renal nerve peak interval sequence in normotensive and hypertensive rats.

作者信息

Zhang T, Johns E J

机构信息

Department of Physiology, Medical School, Birmingham, United Kingdom.

出版信息

Clin Exp Pharmacol Physiol. 1998 Nov;25(11):896-903. doi: 10.1111/j.1440-1681.1998.tb02340.x.

Abstract
  1. The use of non-linear dynamic analysis for the measurement of control processes in low-dimensional signals, for example, blood pressure and heart rate variability, are well established and accepted. However, the application of these analytical techniques to a high-dimensional signal, such as renal sympathetic nerve activity (RSNA), has not been validated. 2. The present study set out to develop an approach whereby the high-dimensional signal of RSNA was reduced to a low-dimensional one by extracting the peak interval sequence (PIS), using Cluster analysis, in order to allow the use of non-linear dynamics analysis. Brachial nerves were electrically stimulated (1.6 Hz, 0.2 ms, 15 V) to elicit a sympatho-excitation in groups of anaesthetized normotensive Wistar and stroke-sprone spontaneously hypertensive rats (SHRSP). 3. It was found that, under basal conditions, the correlation dimension, D2, was stable over a range of embedding dimensions from 12 to 25. Moreover, the largest Lyapunov exponent had a small positive value that was also stable over these embedding dimensions. These values showed that the signal was of low dimensionality and that chaos was present. 4. In Wistar rats, brachial nerve stimulation significantly (P < 0.05-0.001) increased blood pressure (by 25%), heart rate (by 5%) and RSNA (by 200%), which was associated with significant (P < 0.05) reductions in the correlation dimension D2 and the largest Lyapunov exponent of the PIS generated from the renal nerve signal. In contrast, in SHRSP, there were similar increases in blood pressure, heart rate and RSNA in response to brachial nerve stimulation, but neither the correlation dimension nor largest Lyapunov exponent was altered. 5. These findings demonstrate that by extracting the PIS from the renal sympathetic nerve signal, the application of non-linear chaos analysis makes it possible to distinguish differences in the pattern of reflexly induced excitation in sympathetic traffic to the kidney in the pathophysiological state of hypertension. Whether this applies to sympathetic outflow to other organs and tissues remains to be investigated.
摘要
  1. 将非线性动力学分析用于测量低维信号(例如血压和心率变异性)中的控制过程,这一做法已得到充分确立和认可。然而,这些分析技术应用于高维信号(如肾交感神经活动(RSNA))尚未得到验证。2. 本研究旨在开发一种方法,通过使用聚类分析提取峰间隔序列(PIS),将RSNA的高维信号降为低维信号,以便能够使用非线性动力学分析。对麻醉的正常血压Wistar大鼠和易中风的自发性高血压大鼠(SHRSP)进行分组,电刺激臂神经(1.6 Hz,0.2 ms,15 V)以引发交感兴奋。3. 研究发现,在基础条件下,关联维数D2在12至25的一系列嵌入维数范围内是稳定的。此外,最大Lyapunov指数具有较小的正值,在这些嵌入维数范围内也稳定。这些值表明该信号是低维的且存在混沌。4. 在Wistar大鼠中,臂神经刺激显著(P < 0.05 - 0.001)升高血压(升高25%)、心率(升高5%)和RSNA(升高200%),这与肾神经信号产生的PIS的关联维数D2和最大Lyapunov指数显著(P < 0.05)降低有关。相比之下,在SHRSP中,臂神经刺激引起的血压、心率和RSNA有类似升高,但关联维数和最大Lyapunov指数均未改变。5. 这些发现表明,通过从肾交感神经信号中提取PIS,应用非线性混沌分析能够区分高血压病理生理状态下肾交感神经活动中反射性诱发兴奋模式的差异。这是否适用于交感神经向其他器官和组织的输出仍有待研究。

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