An Empirical Mode Decomposition approach for automated diagnosis of migraine

2022 ◽  
Vol 73 ◽  
pp. 103413
Author(s):  
Zülfikar Aslan
Keyword(s):  
Empirical Mode Decomposition ◽  
Automated Diagnosis ◽  
Decomposition Approach ◽  
Mode Decomposition

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Speech Enhancement: A Multivariate Empirical Mode Decomposition Approach

2013 ◽  
pp. 192-199 ◽  
Author(s):  
Jordi Solé-Casals ◽  
Esteve Gallego-Jutglà ◽  
Pere Martí-Puig ◽  
Carlos M. Travieso ◽  
Jesús B. Alonso
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Enhancement ◽  
Decomposition Approach ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

2018 ◽  
Vol 94 ◽  
pp. 11-18 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Joel En Wei Koh ◽  
Yuki Hagiwara ◽  
Jen Hong Tan ◽  
Arkadiusz Gertych ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Automated Diagnosis ◽  
Mode Decomposition

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

2021 ◽  
pp. 095965182110205
Author(s):  
Alireza Moezi ◽  
Seyed Mohamad Kargar
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Wavelet Transforms ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Computational Time ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Mode Decomposition

This article proposed a practical approach to isolating faults in analog circuits. The contribution of this article is twofold. First, the optimized empirical mode decomposition approach is presented based on the Hellinger distance such that there is a minimum dependency between intrinsic mode functions. Features with high distinction could be extracted by employing intrinsic mode functions in fault detection problem of analog benchmark circuits. Second, the non-dominated sorting genetic algorithm is employed to retain excellent features and speed up the execution, resulting in the high accuracy of fault detection and isolation. The number of features and mean squared error are selected as objective functions. The features from the data are also extracted using the fast Fourier and wavelet transforms for comparison. Finally, the support vector machine and artificial neural network are employed to isolate faults. Two circuits under test are simulated, and the output signals of the faulty and fault-free circuits are extracted by the Monte Carlo analysis. According to the obtained simulation results, the proposed method with a low-dimensional feature vector outperformed the previous methods, and the computational time has also reduced significantly.

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