Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Chang, Kuei-Hu

doi:10.3390/s100606063

Cited by 168 publications

(80 citation statements)

References 21 publications

(26 reference statements)

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“…In the past, some interesting EMD procedures have been proposed to automatically select and classify the IMFs of interest. They can be divided into two categories: (i) methods based on low-pass filtering [44][45][46] and (ii) methods based on thresholding filtering [47,48]. EMD based on low-pass filtering considers that the IMFs are decomposed into two sets: the first set of IMFs represents only the signal of interest and the second set stands for the noise and artifacts.…”

Section: Selection Of Components Of Interestmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

148

122

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Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

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Section: Selection Of Components Of Interestmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

148

122

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“…3.Here other than white Gaussian is also added to ECG signal to check the performance of EEMD algorithm. Three different patterns of added noise are [4] …”

Section: Experiments and Resultsmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction

Agarwal¹

2013

IOSR-JECE

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“…The electrocardiogram (ECG) has been widely used for diagnosis purposes of heart diseases [5]. Good quality ECG is utilized by the physicians for interpretation and identification of physiological and pathological phenomena.…”

Section: Ecg Signalmentioning

confidence: 99%