Variational mode decomposition denoising combined the detrended fluctuation analysis

Liu, Yuanyuan; Yang, Ge; Li, Ming; Yin, Hong

doi:10.1016/j.sigpro.2016.02.011

Cited by 255 publications

(120 citation statements)

References 21 publications

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“…DFA slope α 0 = 0.5, 1.0 and 1.5 depending upon the type of noise to be white Gaussian noise, pink or Brownian noise respectively. To cope up with the problem of mode mixing, the value of scaling exponent (α 0 ) has been set to be 0.75 [22,23]. Hence, the effect of noise is reduced by selecting the VMFs, which are found to be free from noise.…”

Section: Methodology Of the Proposed Frameworkmentioning

confidence: 99%

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

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Section: Methodology Of the Proposed Frameworkmentioning

confidence: 99%

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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“…The detailed theoretical background and applications in simulated signal analysis of VMD technique can be referred to Dragomiretskiy and Zosso; Xue et al; and Liu et al The current signal decomposition methods are limited by (a) the algorithms lack mathematical theory, that is, EMD; (b) the vulnerability to noise effect; and (c) the band limits of wavelet approaches and the corresponding filters are not easy to define. In this study, VMD is applied to decompose the measured acceleration signal

{\ddot{x}}_{p} ((), t)

into a limited number of IMFs, which will be used for the subsequent modal identification of time‐varying structures.…”

Section: Theoretical Background Of the Proposed Approachmentioning

confidence: 99%

Time‐varying system identification using variational mode decomposition

Hao

et al. 2018

Struct Control Health Monit

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Summary A new time‐varying system identification approach is proposed in this paper by using variational mode decomposition. The newly developed variational mode decomposition technique can decompose the measured responses into a limited number of intrinsic mode functions, and the instantaneous frequencies of time‐varying systems are identified by the Hilbert transform of each intrinsic mode function. Numerical and experimental verifications are conducted to demonstrate the effectiveness and accuracy of using the proposed approach for time‐varying system identification, that is, to obtain the instantaneous frequency. Numerical studies on a structural system with time‐varying stiffness are conducted. Experimental validations on analyzing the measured vibration data in the laboratory from a steel frame structure and a time‐varying bridge–vehicle system are also conducted. The results from the presented technique are compared with those from empirical mode decomposition‐based methods, which verify that the developed approach can identify the instantaneous frequencies with a better accuracy.

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“…The intrinsic mode functions (IMFs) of different frequencies are obtained by nonrecursive decomposition [17]. The implementation of VMD is divided into two parts: the construction of variational problem and the solution of variational problem [18]. The first part is the construction of variational problem.…”

Section: A Description Of Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN

Liu

Jing

2018

Complexity

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Electromechanical actuators (EMAs) are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition (VMD) multifractal detrended fluctuation analysis (MFDFA) and probabilistic neural network (PNN). First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions (IMFs). Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis (PCA) was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network (PNN) was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction.

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Variational mode decomposition denoising combined the detrended fluctuation analysis

Cited by 255 publications

References 21 publications

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Time‐varying system identification using variational mode decomposition

Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN

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