An Empirical Mode Decomposition approach for automated diagnosis of migraine

2022 ◽  
Vol 73 ◽  
pp. 103413
Author(s):  
Zülfikar Aslan
Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen

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.


2018 ◽  
Vol 94 ◽  
pp. 11-18 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Joel En Wei Koh ◽  
Yuki Hagiwara ◽  
Jen Hong Tan ◽  
Arkadiusz Gertych ◽  
...  

Author(s):  
Alireza Moezi ◽  
Seyed Mohamad Kargar

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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