Huikuri Heikki V, Mäkikallio Timo H, Perkiömäki Juha
Division of Cardiology, Department of Internal Medicine, University of Oulu, Finland.
J Electrocardiol. 2003;36 Suppl:95-9. doi: 10.1016/j.jelectrocard.2003.09.021.
Heart rate (HR) variability has been conventionally analyzed with time and frequency domain methods, which measure the overall magnitude of R-R interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of HR dynamics by methods based on chaos theory and nonlinear system theory has gained recent interest. This interest is based on observations suggesting that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent observational studies suggest that some indexes describing nonlinear HR dynamics, such as fractal scaling exponents, may provide more powerful prognostic information than the traditional HR variability indexes. In particular, short-term fractal scaling exponent measured by detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Approximate entropy, a nonlinear index of HR dynamics, which describes the complexity of R-R interval behavior, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indexes, eg, Lyapunov exponent and correlation dimensions, which also give information on the characteristics of HR dynamics, but their clinical utility is not well established. Although concepts of chaos theory, fractal mathematics, and complexity measures of HR behavior in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for future research to expand our knowledge concerning the behavior of cardiovascular oscillations in normal healthy conditions as well as in disease states.
心率（HR）变异性传统上是用时域和频域方法进行分析的，这些方法测量R-R间期围绕其平均值波动的总体幅度或某些预定频率下的波动幅度。基于混沌理论和非线性系统理论的方法对HR动态进行分析最近受到了关注。这种关注基于一些观察结果，这些结果表明参与心血管调节的机制可能以非线性方式相互作用。此外，最近的观察性研究表明，一些描述非线性HR动态的指标，如分形标度指数，可能比传统的HR变异性指标提供更有力的预后信息。特别是，通过去趋势波动分析方法测量的短期分形标度指数已被证明可以预测不同人群中的致命心血管事件。近似熵是HR动态的一个非线性指标，它描述了R-R间期行为的复杂性，已提供了有关心房颤动易感性的信息。还有许多其他非线性指标，例如李雅普诺夫指数和关联维数，它们也给出了有关HR动态特征的信息，但其临床效用尚未得到充分确立。尽管混沌理论、分形数学以及HR行为与心血管生理学或各种心血管事件相关的复杂性测量等概念与临床医学仍相距甚远，但它们是未来研究富有成果的领域，有助于扩展我们对正常健康状况以及疾病状态下心血管振荡行为的认识。
