Journal ArticleDOI
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding
TLDR
In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal, and it is shown that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the E MD decomposition mode.Abstract:
One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.read more
Citations
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Journal ArticleDOI
A review on empirical mode decomposition in fault diagnosis of rotating machinery
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.
Journal ArticleDOI
EEG artifact removal?state-of-the-art and guidelines
TL;DR: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second- order blind identification (SOBI).
Journal ArticleDOI
Filter Bank Property of Multivariate Empirical Mode Decomposition
TL;DR: It is found that, similarly to EMD, MEMD also essentially acts as a dyadic filter bank on each channel of the multivariate input signal, but better aligns the corresponding intrinsic mode functions from different channels across the same frequency range which is crucial for real world applications.
Journal ArticleDOI
Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains
TL;DR: The proposed method to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI
Ideal spatial adaptation by wavelet shrinkage
TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.
Book
Time-Frequency Analysis
TL;DR: In this article, the authors present a general approach and the Kernel Method for reduced interference in the representation of signal signals, which is based on the Wigner distribution and the characteristic function operator.
Book ChapterDOI
Time-Frequency Analysis
TL;DR: In this article, it is shown that the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.
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