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Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

DOI

  • Khuram Naveed, COMSATS University Islamabad
    • ,
  • Sidra Mukhtar, COMSATS University Islamabad
    • ,
  • Naveed Ur Rehman

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).

OriginalsprogEngelsk
Titel2021 IEEE Statistical Signal Processing Workshop, SSP 2021
Antal sider5
ForlagIEEE
Udgivelsesårjul. 2021
Sider441-445
ISBN (Elektronisk)9781728157672
DOI
StatusUdgivet - jul. 2021
Begivenhed21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brasilien
Varighed: 11 jul. 2021 → 14 jul. 2021

Konference

Konference21st IEEE Statistical Signal Processing Workshop, SSP 2021
LandBrasilien
ByVirtual, Rio de Janeiro
Periode11/07/2021 → 14/07/2021
SerietitelIEEE Workshop on Statistical Signal Processing Proceedings
Vol/bind2021-July

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