EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

Download Full-text

Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting

Measurement ◽

10.1016/j.measurement.2017.02.007 ◽

2017 ◽

Vol 103 ◽

pp. 52-61 ◽

Cited By ~ 43

Author(s):

Yusuf Yaslan ◽

Bahadır Bican

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Load Forecasting ◽

Support Vector ◽

Denoising Method ◽

Mode Decomposition ◽

Electricity Load ◽

Electricity Load Forecasting

Download Full-text

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Entropy ◽

10.3390/e21020145 ◽

2019 ◽

Vol 21 (2) ◽

pp. 145 ◽

Cited By ~ 5

Author(s):

Viet Tra ◽

Bach-Phi Duong ◽

Jae-Young Kim ◽

Muhammad Sohaib ◽

Jong-Myon Kim

Keyword(s):

Fault Diagnosis ◽

Storage Tank ◽

Wavelet Packet ◽

Relative Energy ◽

Classification Performance ◽

Support Vector ◽

Diagnostic Model ◽

Spherical Tank ◽

Useful Components ◽

Diagnosis Model

This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study.

Download Full-text

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽

10.3390/electronics8060597 ◽

2019 ◽

Vol 8 (6) ◽

pp. 597 ◽

Cited By ~ 4

Author(s):

Guohui Li ◽

Zhichao Yang ◽

Hong Yang

Keyword(s):

Signal To Noise Ratio ◽

Noise Signal ◽

Ensemble Empirical Mode Decomposition ◽

Measurement Technology ◽

Signal Denoising ◽

Frequency Noise ◽

Denoising Method ◽

Radiated Noise ◽

Mode Decomposition ◽

Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

Download Full-text

Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect

International Journal of Antennas and Propagation ◽

10.1155/2015/769478 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

Feng-Ping An ◽

Da-Chao Lin ◽

Xian-Wei Zhou ◽

Zhihui Sun

Keyword(s):

Empirical Mode Decomposition ◽

Edge Effect ◽

Signal To Noise Ratio ◽

Random Noise ◽

Gaussian White Noise ◽

Support Vector ◽

Mode Decomposition ◽

Expansion Technique ◽

Bidimensional Empirical Mode Decomposition ◽

Adaptive Ability

Bidimensional empirical mode decomposition (BEMD) algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model constructed by the support vector machine (SVM) method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio.

Download Full-text

Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/658501 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Xiaoou Li ◽

Yuning Yan ◽

Wenshi Wei

Keyword(s):

Working Memory ◽

Memory Load ◽

Treatment Strategies ◽

Classification Performance ◽

Support Vector ◽

Healthy Controls ◽

Stroke Patients ◽

Mode Decomposition ◽

Kernel Support Vector Machine ◽

Stimulus Features

The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

Download Full-text

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Energies ◽

10.3390/en10030408 ◽

2017 ◽

Vol 10 (3) ◽

pp. 408 ◽

Cited By ~ 31

Author(s):

Dongxiao Niu ◽

Shuyu Dai

Keyword(s):

Particle Swarm Optimization Algorithm ◽

Support Vector ◽

Short Term ◽

Denoising Method ◽

Swarm Optimization ◽

Modified Particle Swarm Optimization ◽

Relational Analysis ◽

Mode Decomposition ◽

Short Term Load Forecasting ◽

Grey Relational

Download Full-text

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

Applied Sciences ◽

10.3390/app112210943 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10943

Author(s):

Zhili Chen ◽

Peng Wang ◽

Zhixian Gui ◽

Qinghui Mao

Keyword(s):

Signal To Noise Ratio ◽

Solution Process ◽

Vertical Component ◽

Microseismic Monitoring ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Microseismic Data ◽

Mode Decomposition ◽

Processing Step

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

Download Full-text

Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery

Structural Health Monitoring ◽

10.1177/1475921719887496 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1453-1470

Author(s):

Ali Dibaj ◽

Mir Mohammad Ettefagh ◽

Reza Hassannejad ◽

Mir Biuok Ehghaghi

Keyword(s):

Principal Component ◽

Signal Decomposition ◽

Support Vector ◽

Variational Mode Decomposition ◽

Signal Processing Technique ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Optimal Values ◽

The Mean ◽

Mixing Problem

Variational mode decomposition is a powerful signal processing technique that can adaptively decompose a multi-component signal into a number of modes, via solving an optimization problem. The optimal performance of this method in signal decomposition and avoiding of the mode mixing problem strictly relies on the true selection of decomposition parameters, that is, the number of extracted modes ( K) and the mode frequency bandwidth control parameter ( α). In the literature, the optimal values of these parameters are achieved by evaluating fault-related indices like kurtosis, but such an index is inefficient in judging the decomposition of healthy (without fault-related components), low-defective, and high-noise signals. In this research, a novel method called fine-tuned variational mode decomposition is proposed to determine the optimal values of decomposition parameters K and α, by judging the adaptive indices. In this proposed method, the optimal values of these parameters are obtained by minimizing the mean bandwidth of the extracted modes. In order to achieve these optimal values, the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, should not exceed of defined thresholds for optimal values. The proposed method is applied to the simulation signal and experimental ones collected from the automobile gearbox system. Comparing this method with those in the literature exhibits its higher effectiveness in the true decomposition of signals with different natures. It is also shown that using the proposed method for signal decomposition is able to correctly classify the healthy and defective states of the gearbox system alongside the principal component analysis method and support vector machine classifier.

Download Full-text

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211063688 ◽

2021 ◽

pp. 014233122110636

Author(s):

Dongmei Wang ◽

Lijuan Zhu ◽

Jikang Yue ◽

Jingyi Lu ◽

Gongfa Li

Keyword(s):

Search Algorithm ◽

Signal To Noise Ratio ◽

Decomposition Algorithm ◽

Signal Denoising ◽

Two Dimensional ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Mode Decomposition ◽

Penalty Factor ◽

Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

Download Full-text