Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals

被引:339
作者
Mandic, Danilo P. [1 ,2 ,3 ]
Rehman, Naveed Ur
Wu, Zhaohua [4 ]
Huang, Norden E. [5 ,6 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, London SW7 2AZ, England
[2] Katholieke Univ Leuven, Louvain, Belgium
[3] RIKEN, Wako, Saitama, Japan
[4] Florida State Univ, Tallahassee, FL 32306 USA
[5] Natl Cent Univ, Chungli, Taiwan
[6] Acad Sinica, Taipei, Taiwan
基金
英国工程与自然科学研究理事会; 美国国家科学基金会;
关键词
HILBERT SPECTRUM;
D O I
10.1109/MSP.2013.2267931
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article addresses data-driven time-frequency (T-F) analysis of multivariate signals, which is achieved through the empirical mode decomposition (EMD) algorithm and its noise assisted and multivariate extensions, the ensemble EMD (EEMD) and multivariate EMD (MEMD). Unlike standard approaches that project data onto predefined basis functions (harmonic, wavelet) thus coloring the representation and blurring the interpretation, the bases for EMD are derived from the data and can be nonlinear and nonstationary. For multivariate data, we show how the MEMD aligns intrinsic joint rotational modes across the intermittent, drifting, and noisy data channels, facilitating advanced synchrony and data fusion analyses. Simulations using real-world case studies illuminate several practical aspects, such as the role of noise in T-F localization, dealing with unbalanced multichannel data, and nonuniform sampling for computational efficiency.
引用
收藏
页码:74 / 86
页数:13
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