Multivariate empirical mode decomposition
Multivariate empirical mode decomposition
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DOI：
10.1098/rspa.2009.0502
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发表时间：
2010-05-08
影响因子：
3.5
通讯作者：
Mandic, D. P.
中科院分区：
文献类型：
--
作者：
Rehman, N.;Mandic, D. P.
尽管经验模式分解（EMD）已成为非线性和非平稳信号时间频率分析的事实上的标准，但其多元扩展只是出现的。但是，它们是直接多通道数据分析的先决条件。朝这个方向的重要一步是计算本地均值，因为对于多元信号，局部极值的概念没有很好地定义。为此，我们建议在高音（N-SPHERES）上使用多个方向使用实值的投影，以计算多元信号的信封和局部均值，从而导致EMD的多元扩展。为了生成一组合适的方向向量，根据均匀的角度采样方法和Quasi-Monte carlo Carlo基于Carlo的低静电序列对单位超球（N-SPHERES（N-SPHERES）进行采样。在六角形合成和现实世界人类运动信号上进行的模拟证明了所提出的算法在多元数据中找到常见振荡模式的潜力。
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local extrema is not well defined for multivariate signals. To this end, we propose to use real-valued projections along multiple directions on hyperspheres (n-spheres) in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD. To generate a suitable set of direction vectors, unit hyperspheres (n-spheres) are sampled based on both uniform angular sampling methods and quasi-Monte Carlo-based low-discrepancy sequences. The potential of the proposed algorithm to find common oscillatory modes within multivariate data is demonstrated by simulations performed on both hexavariate synthetic and real-world human motion signals.