  We propose a novel multivariate signal denoising method that performs
long-range correlation analysis of multiple modes in input data by considering
inherent inter-channel dependencies of the data. That is achieved through a
novel and generic multivariate extension of detrended fluctuation analysis
(DFA) method - another contribution of this paper. Specifically, our proposed
denoising method first obtains data driven multiscale signal representation
using multivariate variational mode decomposition (MVMD) method. Then, the
proposed generic multivariate DFA is used to reject the predominantly noisy
modes based on their randomness scores. Finally, the denoised signal is
reconstructed by summing the remaining modes albeit after the removal of the
noise traces using the principal component analysis (PCA).
