Blind separation of ECG signals from noisy signals affected by electrosurgical artifacts

Bensafia, Kahina; Mansour, Ali; Boudraa, Abdel-Ouahab; Haddab, Salah; Ariès, Philippe; Clément, Benoı̂t

doi:10.1007/s10470-020-01674-1

Cited by 11 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Empirical mode decomposition (EMD) is a fully data-driven method to decompose real world multicomponent signals into a reduced number of intrinsic mode functions (IMFs), which are typically AM-FM signals from which meaningful instantaneous amplitude and frequency are estimated [1], [2], [3], [4]. Properties of this decomposition, such as locality, completeness, data-driven nature and multi-resolution aspect, have made EMD a valuable tool for real world non-stationary signals analysis and particularly in deterministic situations [5], [6], [7], [8], [9], [10], [11]. For stochastic situations involving broadband noise, namely, fractional Gaussian noise (fGn), EMD acts as dyadic filter bank [12].…”

Section: Introductionmentioning

confidence: 99%

On the Behavior of MEMD in Presence of Multivariate Fractional Gaussian Noise

Komaty¹,

Boudraa

Flandrin

et al. 2021

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

Multivariate empirical mode decomposition (MEMD) has been introduced to make standard EMD suitable for direct multichannel signals processing. Unlike EMD, MEMD is able to align sifted intrinsic mode functions (IMFs) from multiple data channels. The aim of this work is to analyze the behavior of MEMD under multivariate fGn (MfGn) with different Hurst exponents, H, and strengths of link between pairs of channels, ρ, of each sifted IMF. We report results supporting the claim that, regardless of ρ values and for both MfGns long-range and short-range dependent, MEMD acts as filter bank on each channel of the input multivariate signal. Whatever the ρ and H values, this equivalent filter bank structure is dyadic with constant-Q band-pass filters. The observed self-similar filter bank structure leads to a deeper statistical studies of the variance distribution and zero-crossings alignment in order to express this self-similarity in terms of spectral densities of multidimensional IMFs. These statistical properties generalize what was previously conducted for EMD to MEMD and estimation strategy of H exponent is proposed. The filter bank behavior of MEMD is illustrated on real turbulent flow data and the estimated H exponent brings out the long-range-dependent nature of the turbulent flow data. An application to EEG data is also proposed.

show abstract