Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

TL;DR

This paper introduces a new multivariate signal denoising approach that leverages a generic multivariate extension of detrended fluctuation analysis, combined with multivariate variational mode decomposition and PCA, to effectively reduce noise while preserving signal integrity.

Contribution

It presents a novel multivariate extension of DFA and integrates it with MVMD and PCA for improved multichannel signal denoising.

Findings

Effective noise rejection demonstrated on multichannel signals

Preserves key signal features after denoising

Outperforms traditional univariate methods

Abstract

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).