Weighted Window Sliding Multivariate Empirical Mode Decomposition for Online Multichannel Filtering

Abstract

Affected by nonlinear and non-stationary problems, classical linear analysis approaches may fail in analyzing real-world signals, such as the biomedical data. As a fully data-driven and unsupervised signal analysis algorithm, the empirical mode decomposition (EMD) has been developed and applied widely in various fields, especially for de-noising filtering. Due to the increasing need of high speed continuous sampling for multichannel signals, it is necessitated for the more efficient real-time filtering approach to deal with the large-scale time series data. Unfortunately, although the multivariate empirical mode decomposition (MEMD) performs better for multichannel analysis, it is extremely time-consuming to realize the signal time-frequency decomposition. Therefore, in this paper, a novel online multidimensional filtering algorithm combined with MEMD is proposed. With weighted sliding window, it not only improves the real time property for multichannel de-noising, but also realizes the continuous smooth filtering. Moreover, a novel statistic-based algorithm is also proposed to determine the optimal window size quantitatively. Finally, the proposed novel multichannel filtering approach is validated and analyzed by utilizing the real measured electrooculography data and wind power generation system operation data.