Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method

@article{Wu2009EnsembleEM,
  title={Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method},
  author={Zhaohua Wu and Norden E. Huang},
  journal={Adv. Data Sci. Adapt. Anal.},
  year={2009},
  volume={1},
  pages={1-41}
}
  • Zhaohua Wu, N. Huang
  • Published 2009
  • Mathematics, Computer Science
  • Adv. Data Sci. Adapt. Anal.
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space… Expand
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Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Expand
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Morphological Filter-Assisted Ensemble Empirical Mode Decomposition
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The proposed morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD. Expand
Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing
TLDR
A partly ensemble EMD (PEEMD) method is proposed to resolve the mode mixing problem and can eliminate the residue noise in the IMFs effectively and generates IMFs with better performance, and represents a sound improvement over the original EMD, EEMD and CEEMD. Expand
Fast ensemble empirical mode decomposition for speech-like signal analysis using shaped noise addition
TLDR
The experimental results show that both pink noise and brown noise outperform the white noise in terms of computation for the EEMD of speech-like signal, and the signal-spectrum-dependent noise is able to effectively randomize the targeted signal in time domain, and then significantly save the superfluous calculation around the corresponding energy-free frequencies. Expand
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References

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Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method
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Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Expand
Noise Corruption of Empirical Mode Decomposition and its Effect on Instantaneous Frequency
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In this work, the extraction of modes containing both signal and noise is identified as the cause of poor IF estimation and the mechanism is shown to be dependent on spectral leak between modes and the phase of the underlying signal. Expand
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  • Zhaohua Wu, N. Huang
  • Mathematics
  • Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
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Based on numerical experiments on white noise using the empirical mode decomposition (EMD) method, we find empirically that the EMD is effectively a dyadic filter, the intrinsic mode function (IMF)Expand
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Noise-modulated EEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation, and may serve as a new adaptive threshold amplitude filtering. Expand
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TLDR
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TLDR
Noise-assisted data is proposed to disturb the EMD error in the sifting process, and the method treats the mean as the final true result. Expand
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TLDR
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TLDR
This paper conjecture that, as the (pre-assigned and fixed) sifting number is changed from a small number to infinity, the EMD corresponds to filter banks with a filtering ratio that changes accordingly from 2 (dyadic) to 1; the filter window does not narrow accordingly, asThe siftingNumber increases. Expand
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TLDR
The results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition. Expand
Ensemble empirical mode decomposition for high frequency ECG noise reduction
  • Kang-Ming Chang
  • Mathematics, Medicine
  • Biomedizinische Technik. Biomedical engineering
  • 2010
TLDR
The proposed EEMD-derived noise reduction performance was observed to be superior to the traditional EMD and IIR filter approaches. Expand
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