Costa Madalena D, Peng Chung-Kang, Goldberger Ary L
Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Avenue, GZ-431, Boston, MA 02215, USA.
Cardiovasc Eng. 2008 Jun;8(2):88-93. doi: 10.1007/s10558-007-9049-1.
Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and non-equilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools--multiscale entropy and multiscale time irreversibility--are able to extract information from cardiac interbeat interval time series not contained in traditional methods based on mean, variance or Fourier spectrum (two-point correlation) techniques. These new methods, with careful attention to their limitations, may be useful in diagnostics, risk stratification and detection of toxicity of cardiac drugs.
心血管信号主要使用传统的时域和频域测量方法进行分析。然而,这些测量方法未能考虑到与多尺度组织和非平衡动力学相关的重要特性。因此,传统信号分析方法与新兴的多尺度技术的互补作用是一个重要的前沿研究领域。本报告的关键发现是,最近开发的两种多尺度计算工具——多尺度熵和多尺度时间不可逆性——能够从心跳间期时间序列中提取传统方法(基于均值、方差或傅里叶频谱(两点相关性)技术)所不包含的信息。这些新方法,在仔细注意其局限性的情况下,可能在心脏药物的诊断、风险分层和毒性检测中有用。