Multivariate Multiscale Entropy Analysis
Multivariate Multiscale Entropy Analysis
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DOI：
10.1109/lsp.2011.2180713
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发表时间：
2012-02-01
影响因子：
3.9
通讯作者：
Mandic, Danilo P.
中科院分区：
文献类型：
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作者：
Ahmed, Mosabber Uddin;Mandic, Danilo P.
多变量的物理和生物记录是常见的，它们的同步分析是理解潜在信号产生机制复杂性的先决条件。传统的熵度量对于随机过程是最大化的，并且不能量化真实的世界数据中的内在长程依赖性，这是复杂系统的一个关键特征。最近引入的多尺度熵（MSE）是一种能够检测内在相关性的单变量方法，并已被用于测量单通道生理信号的复杂性。为了将这种方法推广到多通道数据，我们首先引入多变量样本熵（MSampEn），并在多个时间尺度上对其进行评估，以执行多变量多尺度熵（MMSE）分析。这使得可以评估多变量物理或生理系统的结构复杂性，以及分析中的更多自由度和增强的严谨性。多元合成数据和真实的世界的姿态摇摆分析的模拟支持的方法。
Multivariate physical and biological recordings are common and their simultaneous analysis is a prerequisite for the understanding of the complexity of underlying signal generating mechanisms. Traditional entropy measures are maximized for random processes and fail to quantify inherent long-range dependencies in real world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) is a univariate method capable of detecting intrinsic correlations and has been used to measure complexity of single channel physiological signals. To generalize this method for multichannel data, we first introduce multivariate sample entropy (MSampEn) and evaluate it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis. This makes it possible to assess structural complexity of multivariate physical or physiological systems, together with more degrees of freedom and enhanced rigor in the analysis. Simulations on both multivariate synthetic data and real world postural sway analysis support the approach.