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WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2011 The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely ...
Faltermeier, R +4 more
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Quadrivariate Empirical Mode Decomposition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010We introduce a quadrivariate extension of Empirical Mode Decomposition (EMD) algorithm, termed QEMD, as a tool for the time-frequency analysis of nonlinear and non-stationary signals consisting of up to four channels. The local mean estimation of the quadrivariate signal is based on taking real-valued projections of the input in different directions in
Naveed ur Rehman, Danilo P. Mandic
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Shunt sound decomposition by empirical mode decomposition
2020 IEEE REGION 10 CONFERENCE (TENCON), 2020Unpredictable disequilibrium syndrome and blood pressure fluctuations can occur during hemodialysis therapy. We have proposed a method for analyzing shunt sounds using EMD to predict these symptoms. The shunt sound is the sound of turbulent blood flow generated in the shunt, which can be measured from the puncture needle of the dialyzer.
Yuki Otake, Osamu Sakata
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Flow Empirical Mode Decomposition
2021Decomposing non-stationary signals using Empirical Mode Decomposition (EMD) highly facilitates signal analyses and processing. According to the original algorithm, EMD decomposes the input signal into useful Intrinsic Mode Functions (IMFs). However, EMD has some drawbacks.
Dário Pedro +4 more
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Online Empirical Mode Decomposition
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017The success of Empirical Mode Decomposition (EMD) resides in its practical approach to dissect non-stationary data. EMD repetitively goes through the entire data span to iteratively extract Intrinsic Mode Functions (IMFs). This approach, however, is not suitable for data stream as the entire data set has to be reconsidered every time a new point is ...
Romain Fontugne +2 more
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Sliding Empirical Mode Decomposition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic
Rupert Faltermeier +5 more
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Performance enhancement of ensemble empirical mode decomposition
Mechanical Systems and Signal Processing, 2010 Ensemble empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD).
Ruqiang Yan, Robert X Gao, Zhihua Feng
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Empirical Mode Decomposition - an introduction
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes.
Angela Zeiler +5 more
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NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2010The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused
Po-Hsiang Tsui +2 more
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Median ensemble empirical mode decomposition
Signal Processing, 2020Abstract Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm.
Xun Lang +4 more
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