Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

Khuram Naveed, Sidra Mukhtar, Naveed Ur Rehman

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

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).

Original languageEnglish
Title of host publication2021 IEEE Statistical Signal Processing Workshop, SSP 2021
Number of pages5
PublisherIEEE
Publication dateJul 2021
Pages441-445
ISBN (Electronic)9781728157672
DOIs
Publication statusPublished - Jul 2021
Event21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil
Duration: 11 Jul 202114 Jul 2021

Conference

Conference21st IEEE Statistical Signal Processing Workshop, SSP 2021
Country/TerritoryBrazil
CityVirtual, Rio de Janeiro
Period11/07/202114/07/2021
SeriesIEEE Workshop on Statistical Signal Processing Proceedings
Volume2021-July

Keywords

  • Detrended fluctuation Analysis
  • Multivariate signals
  • Multivariate variational mode decomposition

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