Huikuri Heikki V, Perkiömäki Juha S, Maestri Roberto, Pinna Gian Domenico
Department of Internal Medicine, Institute of Clinical Medicine, Centre of Excellence in Research, University of Oulu, Oulu 90014, Finland.
Philos Trans A Math Phys Eng Sci. 2009 Apr 13;367(1892):1223-38. doi: 10.1098/rsta.2008.0294.
Heart rate variability (HRV) has been conventionally analysed with time- and frequency-domain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by novel methods, such as heart rate turbulence after ventricular premature beats, deceleration capacity of heart rate and methods based on chaos theory and nonlinear system theory, have gained recent interest. Recent observational studies have suggested that some indices describing nonlinear heart rate dynamics, such as fractal scaling exponents, heart rate turbulence and deceleration capacity, may provide useful prognostic information in various clinical settings and their reproducibility may be better than that of traditional indices. For example, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Similarly, heart rate turbulence and deceleration capacity have performed better than traditional HRV measures in predicting mortality in post-infarction patients. Approximate entropy, a nonlinear index of heart rate dynamics, which describes the complexity of RR interval behaviour, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indices which also give information on the characteristics of heart rate dynamics, but their clinical usefulness is not as well established. Although the concepts of nonlinear dynamics, fractal mathematics and complexity measures of heart rate behaviour, heart rate turbulence, deceleration capacity in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for research to expand our knowledge concerning the behaviour of cardiovascular oscillations in normal healthy conditions as well as in disease states.
心率变异性(HRV)传统上采用时域和频域方法进行分析,这些方法测量RR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。通过新方法分析心率动态,如室性早搏后的心率震荡、心率减速能力以及基于混沌理论和非线性系统理论的方法,最近受到了关注。近期的观察性研究表明,一些描述非线性心率动态的指标,如分形标度指数、心率震荡和减速能力,可能在各种临床环境中提供有用的预后信息,并且它们的可重复性可能优于传统指标。例如,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预测不同人群中的致命心血管事件。同样,在预测心肌梗死后患者的死亡率方面,心率震荡和减速能力比传统的HRV测量方法表现更好。近似熵是心率动态的一个非线性指标,它描述了RR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也能提供有关心率动态特征的信息,但其临床实用性尚未得到充分确立。尽管非线性动力学、分形数学以及心率行为、心率震荡、与心血管生理学或各种心血管事件相关的减速能力的复杂性测量等概念与临床医学仍有很大距离,但它们是一个富有成果的研究领域,有助于扩展我们对正常健康状态以及疾病状态下心血管振荡行为的认识。