Ivanov Plamen Ch., Nunes Amaral Luis A., Goldberger Ary L., Havlin Shlomo, Rosenblum Michael G., Stanley H. Eugene, Struzik Zbigniew R.
Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215.
Chaos. 2001 Sep;11(3):641-652. doi: 10.1063/1.1395631.
We explore the degree to which concepts developed in statistical physics can be usefully applied to physiological signals. We illustrate the problems related to physiologic signal analysis with representative examples of human heartbeat dynamics under healthy and pathologic conditions. We first review recent progress based on two analysis methods, power spectrum and detrended fluctuation analysis, used to quantify long-range power-law correlations in noisy heartbeat fluctuations. The finding of power-law correlations indicates presence of scale-invariant, fractal structures in the human heartbeat. These fractal structures are represented by self-affine cascades of beat-to-beat fluctuations revealed by wavelet decomposition at different time scales. We then describe very recent work that quantifies multifractal features in these cascades, and the discovery that the multifractal structure of healthy dynamics is lost with congestive heart failure. The analytic tools we discuss may be used on a wide range of physiologic signals. (c) 2001 American Institute of Physics.
我们探讨统计物理学中发展出的概念可在多大程度上有效地应用于生理信号。我们用健康和病理状况下人类心跳动力学的代表性例子来说明与生理信号分析相关的问题。我们首先回顾基于两种分析方法（功率谱和去趋势波动分析）的近期进展，这两种方法用于量化有噪声的心跳波动中的长程幂律相关性。幂律相关性的发现表明人类心跳中存在尺度不变的分形结构。这些分形结构由不同时间尺度上小波分解揭示的逐搏波动的自仿射级联表示。然后我们描述了最近量化这些级联中多重分形特征的工作，以及发现健康动力学的多重分形结构在充血性心力衰竭时丧失。我们讨论的分析工具可用于广泛种类的生理信号。（c）2001美国物理研究所。
