Facilitating joint chaos and fractal analysis of biosignals through nonlinear adaptive filtering.
Facilitating joint chaos and fractal analysis of biosignals through nonlinear adaptive filtering.
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DOI：
10.1371/journal.pone.0024331
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Tung WW
中科院分区：
文献类型：
--
作者：
Gao J;Hu J;Tung WW
混沌和随机分形理论是全面刻画复杂多尺度生物信号非线性动力学的重要理论之一。混沌分析要求信号相对无噪声、平稳，而分形分析要求信号无节奏、无标度。为了便于对生物信号进行混沌和分维联合分析，我们提出了一种自适应算法，该算法：(1)可以很容易地去除信号中的非平稳性；(2)可以比线性滤波、小波去噪和基于混沌的降噪技术更有效地降低信号中的噪声；(3)可以很容易地将多尺度生物信号分解成一系列固有的带限函数；(4)提出了一种新的分形和多重分形分析方法，当生物信号中含有强烈的振荡成分时，该方法比现有的方法更好。该方法对于分析各种类型的生物信号是一种有价值的、通用的工具。它的有效性通过为脑波动力学提供新的重要见解以及从脑电信号中自动检测癫痫发作的非常高的准确性而得到证明。
Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals.
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影响因子：
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