Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis

Danilo P. Mandic, Naveed Ur Rehman, Zhaohua Wu, Norden E. Huang

研究成果: 雜誌貢獻期刊論文同行評審

388 引文 斯高帕斯(Scopus)

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.

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文章編號6633076
頁(從 - 到)74-86
頁數13
期刊IEEE Signal Processing Magazine
30
發行號6
DOIs
出版狀態已出版 - 2013

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