Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

Khuram Naveed; Sidra Mukhtar; Naveed Ur Rehman

doi:10.1109/SSP49050.2021.9513823

Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

Khuram Naveed, Sidra Mukhtar, Naveed Ur Rehman

Institut for Elektro- og Computerteknologi - Signal Processing and Machine learning - Edison

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

6 Citationer (Scopus)

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).

Originalsprog	Engelsk
Titel	2021 IEEE Statistical Signal Processing Workshop, SSP 2021
Antal sider	5
Forlag	IEEE
Publikationsdato	jul. 2021
Sider	441-445
ISBN (Elektronisk)	9781728157672
DOI	https://doi.org/10.1109/SSP49050.2021.9513823
Status	Udgivet - jul. 2021
Begivenhed	21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brasilien Varighed: 11 jul. 2021 → 14 jul. 2021

Konference

Konference	21st IEEE Statistical Signal Processing Workshop, SSP 2021
Land/Område	Brasilien
By	Virtual, Rio de Janeiro
Periode	11/07/2021 → 14/07/2021

Navn	IEEE Workshop on Statistical Signal Processing Proceedings
Vol/bind	2021-July

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10.1109/SSP49050.2021.9513823

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Citationsformater

@inproceedings{2f47118f741a4a2dbfd2473e44e07c7b,

title = "Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis",

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). ",

keywords = "Detrended fluctuation Analysis, Multivariate signals, Multivariate variational mode decomposition",

author = "Khuram Naveed and Sidra Mukhtar and {Ur Rehman}, Naveed",

year = "2021",

month = jul,

doi = "10.1109/SSP49050.2021.9513823",

language = "English",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

pages = "441--445",

booktitle = "2021 IEEE Statistical Signal Processing Workshop, SSP 2021",

publisher = "IEEE",

note = "21st IEEE Statistical Signal Processing Workshop, SSP 2021 ; Conference date: 11-07-2021 Through 14-07-2021",

}

Naveed, K, Mukhtar, S & Ur Rehman, N 2021, Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. i 2021 IEEE Statistical Signal Processing Workshop, SSP 2021. IEEE, IEEE Workshop on Statistical Signal Processing Proceedings, bind 2021-July, s. 441-445, 21st IEEE Statistical Signal Processing Workshop, SSP 2021, Virtual, Rio de Janeiro, Brasilien, 11/07/2021. https://doi.org/10.1109/SSP49050.2021.9513823

Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. / Naveed, Khuram; Mukhtar, Sidra; Ur Rehman, Naveed.
2021 IEEE Statistical Signal Processing Workshop, SSP 2021. IEEE, 2021. s. 441-445 (IEEE Workshop on Statistical Signal Processing Proceedings, Bind 2021-July).

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

TY - GEN

T1 - Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

AU - Naveed, Khuram

AU - Mukhtar, Sidra

AU - Ur Rehman, Naveed

PY - 2021/7

Y1 - 2021/7

N2 - 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).

AB - 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).

KW - Detrended fluctuation Analysis

KW - Multivariate signals

KW - Multivariate variational mode decomposition

UR - http://www.scopus.com/inward/record.url?scp=85113522919&partnerID=8YFLogxK

U2 - 10.1109/SSP49050.2021.9513823

DO - 10.1109/SSP49050.2021.9513823

M3 - Article in proceedings

AN - SCOPUS:85113522919

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 441

EP - 445

BT - 2021 IEEE Statistical Signal Processing Workshop, SSP 2021

PB - IEEE

T2 - 21st IEEE Statistical Signal Processing Workshop, SSP 2021

Y2 - 11 July 2021 through 14 July 2021

ER -

Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis

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