1 research outputs found
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis
We propose a generic multivariate extension of detrended fluctuation analysis
(DFA) that incorporates interchannel dependencies within input multichannel
data to perform its long-range correlation analysis. We next demonstrate the
utility of the proposed method within multivariate signal denoising problem.
Particularly, our denosing approach first obtains data driven multiscale signal
representation via multivariate variational mode decomposition (MVMD) method.
Then, proposed multivariate extension of DFA (MDFA) is used to reject the
predominantly noisy modes based on their randomness scores. The denoised signal
is reconstructed using the remaining multichannel modes albeit after removal of
the noise traces using the principal component analysis (PCA). The utility of
our denoising method is demonstrated on a wide range of synthetic and real life
signals