应用物理  >> Vol. 2 No. 3 (July 2012)

基于非线性理论的脉搏主波间期序列的识别
Comparative Analyses of PP Wave Intervals Based on Nonlinear Chaotic Theories

DOI: 10.12677/APP.2012.23012, PDF, HTML, 下载: 2,622  浏览: 10,495  国家自然科学基金支持

作者: 韩清鹏:辽宁科技大学,机械工程与自动化学院

关键词: 脉搏主波间期序列混沌代替数据法非平稳信号Lyapunov指数PP Wave Intervals; Chaos; Surrogate Data Method; Non-Stationary Signals; Lyapunov Exponents

摘要: 本文研究采用基于混沌理论的两种非线性参数估计方法(代替数据法和Lyapunov指数估计法)对两组不同生理病理条件下脉搏主波间期序列进行分析。首先对上述两种算法进行介绍,然后对脉搏主波间期序列进行对比分析。分析结果表明,在时域波形上直观相似的非平稳信号,用上述非线性混沌分析的方法可以有效地加以定量区分。对于不同生理病理条件下的脉搏主波间期系列,由代替数据法所得到的特征参数的特征概率值均小于0.05,拒绝随机假设,信号的混沌特性得到辨识,由此可判断出所计算的脉搏信号具有混沌特征;两组信号的最大Lyapunov指数均为正值并有明显差别。根据代替数据法中的概率值的大小和最大Lyapunov指数可以看出,心律不齐患者比正常人员具有更明显的混沌特征。
Abstract:  In the paper, two nonlinear estimation methods based on chaotic theory, surrogate data method and Lyapunov exponents, are used to distinguish the difference of PP wave intervals (time series of the pulse main peaks). After brief introduction of the corresponding algorithms, two typical different healthy state signals of PP wave intervals are compared by using the two methods. The obtained results demonstrate that the signals are distinguished effectively in quantitative way. With surrogate data method, which is applied to identifying the existing chaos of PP intervals of pulse, it is proved that the series of PP intervals of pulse are chaotic. Largest Lyapunov exponents of PP wave intervals are calculated. The Largest Lyapunov exponents of the two kinds of signals are both positive and different from each other. The chaotic character of arrhythmia is much more significant than that of healthy state.

文章引用: 韩清鹏. 基于非线性理论的脉搏主波间期序列的识别[J]. 应用物理, 2012, 2(3): 72-76. http://dx.doi.org/10.12677/APP.2012.23012

参考文献

[1] S. S. Franklin, S. A. Khan and N. D. Wong. Is pulse pressure useful in predicting risk for coronary heart disease. The Framing- ham Heart Study Circulation, 1999, 100(4): 354-360.
[2] K. H. Choi, D. Y. Kim, S. J. Jung and I. H. Kim. A recording of the radial pulse wave system using photoplethysmogram. SICE Annual Conference 2005 in Okayama, Okayama, 8-10 August 2005, WP2-11.
[3] H. Kantz, T. Schreiber. Nonlinear time series analysis. Cambridge: Cambridge University Press, 1999.
[4] Z. J. He, Q. F. Meng and J. Y. Zhao. Time-frequency (scale) analysis and diagnosis for nonstationary dynamic signal of machinery. International Journal of Plant Engineering and Manage- ment, 1996, 1(1): 40-47.
[5] G. S. Meltzer, Y. Y. Ivanov. Fault detection in gear drives with non-stationary rotational speed—Part I: The time-frequency approach. Mechanical Systems and Signal Processing, 2003, 17(5): 1033-1047.
[6] W. Y. Wang, M. J. Harrap. Condition monitoring of rolling element bearings by using cone kernel time-frequency distribution. Processings of the SPIE Conference on Measurement Technology and Intelligent Instrument, Bellingham, 1993: 290-298.
[7] V. Katvonik, L. Stankovic. Instantaneous frequency estimation using the Wigner distribution with varying and data-driven widow length. IEEE Transactions on Signal Processing, 1998, 46(9): 2351- 2325.
[8] M. Casdagli. Nonlinear prediction of chaotic time series. Physica D, 1989, 35(3): 335-356.
[9] R. Engbert. Testing for nonlinearity: The role of surrogate data. Chaos, Solitons & Fractals, 2002, 13(1): 79-84.
[10] M. Banbrook, G. Ushaw and S. McLaughlin. Lyapunov exponents from a time series: A noise-robust extraction algorithm. Chaos, Solitons and Fractals, 1996, 7(7): 973-976.
[11] J. Theiler, S. Eubank and A. Longtin. Testing for nonlinearity in time series: The method of surrogate data. Physica D: Nonlinear Phenomena, 1992, 58(1-4): 77-94.
[12] K. Yonemoto, T. Yanagawa. Estimating the Lyapunov exponent from chaotic time series with dynamic noise. Statistical Metho- dology, 2007, 4(4): 461-480.
[13] M. Kennel, S. Isabelle. Method to distinguish possible chaos from coloured noise and to determine embedding parameters. Physical Review A, 1992, 46(6): 3111-3118.
[14] H. Kantz. A robust method to estimate the maximal Lyapunov exponent of a time series. Physics Letters A, 1994, 185(1): 77- 87.
[15] A. Wolf, J. V. Swift, H. L. Swinney, et al. Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 1985, 16(3): 285-317.
[16] M. T. Rosenstein, J. J. Collins and C. J. Luca. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 1993, 65(1-2): 117-134.