Pattern complexity and nonlinear dynamics in RR-sequences

A. Ripoli, M. Emdin, C. Passino, L. Zyw
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引用次数: 0

Abstract

The analysis of time series measured from nonlinear signals, may be performed either in the phase space or in the tie-domain. The Largest Lyapunov Exponent (LLE) characterises exponential divergence of trajectories in the phase space; fractal analysis is able to describe the complex pattern of a given time series. To evaluate the relation between the dynamic behavior and pattern complexity of the inherent biological system, RR-interval sequences were derived from 24-hour Holter recordings performed in 55 healthy subjects (37/spl plusmn/4 years, 34 males). Pattern fractal analysis (PFD) was computed on the basis of the measured length and diameter of the signal pattern. and LLE was evaluated by the Wolf algorithm. For each subject, the linear regression between computed PFD and LLE measures over the 24-hour period has been computed, extracting the correlation coefficient and the slope of the PFD vs. LLE relation. The strongest linear correlation between LLE and PFD indicates a light link between the system dynamics and the pattern of the extracted signals. This link suggests the possibility of a direct evaluation of nonlinear dynamics, even over short time intervals, exploiting the computationally less expensive PFD.
rr序列的模式复杂度和非线性动力学
从非线性信号测量的时间序列的分析,可以在相空间或领带域进行。最大李雅普诺夫指数(LLE)表征了相空间中轨迹的指数发散;分形分析能够描述给定时间序列的复杂模式。为了评估动态行为与固有生物系统模式复杂性之间的关系,我们从55名健康受试者(37/spl + 4岁,34名男性)的24小时动态心电图记录中获得了rr间隔序列。在测量信号图案长度和直径的基础上计算图案分形分析(PFD)。采用Wolf算法对LLE进行评估。对于每个受试者,计算了24小时内PFD与LLE测量值之间的线性回归,提取了PFD与LLE关系的相关系数和斜率。LLE和PFD之间最强的线性相关性表明系统动力学与提取信号的模式之间存在轻微的联系。这种联系表明,即使在短时间间隔内,也可以利用计算成本较低的PFD直接评估非线性动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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