自发性血压振荡对脑自动调节的参数与非参数传递函数估计。

Michael Jachan, Matthias Reinhard, Linda Spindeler, Andreas Hetzel, Björn Schelter, Jens Timmer
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引用次数: 5

摘要

脑自动调节(CAR)是大脑在动脉血压变化的情况下保持脑血流量不变的一种控制机制。在阻塞性脑血管疾病患者中,CAR受损可能与脑缺血事件的风险增加有关。使用非参数和双参数传递函数估计器,即外源输入的自回归移动平均模型或向量自回归模型,分析自发血压振荡。使用单侧狭窄或闭塞患者的数据比较方法的性能。我们还通过比较数据分区和其他两次测量的患者的数据来分析再现性。结果表明,方法之间没有显著差异(方差分析,p > 0.27),并且三种方法都可以重复地进行CAR测量(肯德尔tau, p < 0.0016)。综上所述,利用自发振荡的方法可以稳定地获得CAR测量结果,并且所提出的参数方法可以为未来CAR测量的在线应用提供服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parametric versus nonparametric transfer function estimation of cerebral autoregulation from spontaneous blood-pressure oscillations.

Cerebral autoregulation (CAR) is a control mechanism of the brain keeping cerebral blood flow constant albeit the arterial blood pressure varies. Impaired CAR may be associated with an increased risk of cerebral ischemic events in patients with obstructive cerebrovascular disease. Spontaneous blood pressure oscillations are analyzed using a nonparametric and two parametric transfer function estimators, i.e. the autoregressive-moving-average model with exogenous inputs or the vector-autoregressive model. Performance of the methods was compared using data from patients with unilateral stenosis or occlusion. We also analyzed reproducibility by comparing partitions of the data an with data from other patients which have been measured twice. Results show that there is no significant difference between methods (ANOVA, p > 0.27), and that CAR measurements can be performed reproducibly (Kendall's tau, p < 0.0016) by all three methods. In conclusion, CAR measurements by means of spontaneous oscillations can be obtained stably and the presented parametric approaches can serve for future online application of CAR measurement.

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