Multivariate empirical mode decomposition and application to multichannel filtering

Fleureau, Julien; Kachenoura, Amar; Albera, Laurent; Nunes, Jean‐Claude; Senhadji, Lotfi

doi:10.1016/j.sigpro.2011.01.018

Cited by 69 publications

(42 citation statements)

References 21 publications

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“…Nowadays, there are many types of EMD and each one has merits and demerits in processing the signal. For example, eXtended-EMD (X-EMD) [29] can produce better results than MEMD and Turning Tangent EMD (2T-EMD) [30] in dealing with multivariate signal denoising. However, in 2011 Rehman et al also proposed N-A EMD.…”

Section: Discussionmentioning

confidence: 99%

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

Huang

Fan

Abbod

et al. 2013

Entropy

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EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients' EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. OPEN ACCESSEntropy 2013, 15 3326 ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

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Section: Discussionmentioning

confidence: 99%

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

Huang

Fan

Abbod

et al. 2013

Entropy

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“…Mandic and Goh (2009) described and showed that local optima of multivariate data are not possible to find directly as in univariate EMD. Moreover, the mean envelopes defining an IMF are rather confusing for multivariate signals (Fleureau et al, 2011a(Fleureau et al, , 2011bRehman and Mandic, 2010;Rilling et al, 2007). Consequently, several algorithms have been proposed to overcome this problem, i.e., rotation invariant complex EMD (RIEMD) (Altaf et al, 2007), bivariate EMD (BEMD) (Rilling et al, 2007), and multivariate EMD (MEMD) (Rehman and Mandic, 2010).…”

Section: Multivariate Emd (Memd) and Noise Assisted Memd (Na-memd)mentioning

confidence: 99%

The VLF-EM imaging of potential collapse on the LUSI embankment

Sungkono¹,

Husein²,

Prasetyo³

et al. 2014

Journal of Applied Geophysics

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“…These have adverse effects on the performance for fault detection. To alleviate these problems, the variants of EMD, such as EEMD, bivariate empirical mode decomposition (BEMD), and multivariate empirical mode decomposition (MEMD) [22][23][24], have been developed. Moreover, there is a lack of theory to clarify these problems.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

Zhuang

Ding

Tan

et al. 2017

Shock and Vibration

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A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that (1) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and (2) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.

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Multivariate empirical mode decomposition and application to multichannel filtering

Cited by 69 publications

References 21 publications

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

The VLF-EM imaging of potential collapse on the LUSI embankment

Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

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