An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

Xiao-dong, Niu; Lu, Li-Rong; Wang, Jian; Han, Xingcheng; Li, Xuan; Wang, Liming

doi:10.1155/2021/8891217

Cited by 11 publications

(6 citation statements)

References 55 publications

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“…From CEEMDAN's decomposition characteristics of noise signals [16], it can be seen that IMF is basically composed of noise and only contains a small amount of signal details. After PCA decomposition, the signal is basically only concentrated in the first principal component.…”

Section: Principal Component Selection Ofmentioning

confidence: 99%

Laser gyro signal filtering by combining CEEMDAN and principal component analysis

Huang¹,

Lu²,

Liu³

2021

J. vibroeng.

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In order to suppress the random shift error of laser gyro and improve the practical precision of inertial navigation system, an improved gyro filtering method is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and principal component analysis (PCA). Firstly, the gyro signal is decomposed by CEEMDAN, and the noise energy of each intrinsic mode function (IMF) is estimated according to the distribution model of noise energy. Then, on basis of noise energy, the principal component analysis is used to remove the noise IMF to achieve the final denoising of gyro signal. In the proposed method, CEEMD can improve the mode mixing and denoising effect of gyro signal. Moreover, PCA is used to decompose each IMF. According to the noise energy, the noise of each IMF is removed adaptively to avoid the selection of noise IMF and better retain the useful information of the signal. The proposed method is completely dependent on the characteristics of gyro signal and has good adaptability and strong denoising ability. Furthermore, the filtered effect of different methods is analyzed by overlapping Allan variance. The experimental results show that the proposed method can suppress the gyro random drift more efficiently, and the effect of removing noise is better than EMD threshold method and EMD correlation coefficient method.

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Section: Principal Component Selection Ofmentioning

confidence: 99%

Laser gyro signal filtering by combining CEEMDAN and principal component analysis

Huang¹,

Lu²,

Liu³

2021

J. vibroeng.

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“…Many scholars are devoted to the study of mode mixing [3,23,[25][26][27][28][29][30][31][32][33][34][35][36][37][38]. The main methods are EMD manifold [3], modulated EEMD [23], masking EMD (M-EMD) [25][26][27], time-varying filtering EMD (TVF-EMD) [28,29], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…TVF-EMD [28,29] decomposes the nonstationary signal into IMFs with fixed bandwidth and then the instantaneous amplitude, frequency, and phase are applied to build the time-frequency spectrum based on the Hilbert transform. In addition, other scholars [3,23,24,[30][31][32][33][34][35][36][37][38] use noiseassisted methods to solve the mode mixing problem, especially ensemble empirical mode decomposition (EEMD) and complete EEMD (CEEMD). Wu and Huang [24] proposed the EEMD in 2009.…”

Section: Introductionmentioning

confidence: 99%

Resolving mode mixing in wheel–rail surface defect detection using EMD based on binary time scale

Hua,

Zhiquan

2023

Meas. Sci. Technol.

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Due to the mode mixing, empirical mode decomposition (EMD) cannot effectively decompose the vibration signal when the signal is intermittent and pulse interference caused by discontinuous vibration. The methods to solve mode mixing often use noise assistance, such as ensemble EMD (EEMD), complete EEMD (CEEMD), etc. These methods can effectively solve mode mixing, but they also have shortcomings. In EEMD, the added noises not only have residual effects and time-consuming. The drawback of CEEMD is that it is difficult to align during set averaging. In this paper, an improved EMD based on binary time scale (EMD-BTS) is proposed for the fault feature extraction of wheel–rail defect detection. Firstly, the generalized intrinsic mode function (GIMF) is defined based on the time-domain characteristics of non-stationary vibration signals. Then, to tackle the drawbacks of EMD which cannot effectively solve mode mixing caused by signal intermittence and pulse interference, the inherent mode is extracted in the EMD-BTS to decompose the raw signals into GIMFs. Finally, the false components generated by over decomposition are combined based on time-domain cross covariance. A simulation case and a actual case of vehicle bogie are utilized to verify the feasibility of the proposed EMD-BTS. The results indicate that the proposed approach exceeds other typical techniques in extracting intermittent fault features of wheel–rail defect detection.

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“…However, identifying epileptic activity in a long-term EEG signal is boring and complex because such activity occupies a small percentage of the whole EEG recording. Several automated techniques were developed to identify the occurrence of a seizure with all its stages, including pre-ictal, ictal, and post-ictal states [14][15][16][17][18][19][20][21][22][23][24][25][26]. Some studies investigated the use of empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) in classifying epileptic EEG signals [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], experimental results showed an accuracy of 97.75% for classifying epileptic EEG signals. However, EMD techniques have several drawbacks such as the absence of a formal mathematical framework that allows a theoretical analysis and performance evaluation along with the mode mixing problem [16]. In [17], a cascade adaptive filter design was employed to predict multiple signal samples from short-term EEG signals, providing an accuracy of 97.88% for seizure versus seizure-free classification.…”

Section: Introductionmentioning

confidence: 99%

Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network

Eltrass¹,

Tayel²,

Elqady³

2022

J. Neural Eng.

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Context. Epilepsy is the most widespread disorder of the nervous system, affecting humans of all ages and races. The most common diagnostic test in epilepsy is the Electroencephalogram (EEG). Objective. In this paper, a novel automated Deep Learning (DL) approach based on integrating a pre-trained Convolutional Neural Network (CNN) structure, called AlexNet, with the Constant-Q Non-Stationary Gabor Transform (CQ-NSGT) algorithm is proposed for classifying seizure versus seizure-free EEG records. Approach. The CQ-NSGT method is introduced to transform the input 1-D EEG signal into 2-D spectrogram which is sent to the AlexNet CNN model. The AlexNet architecture is utilized to capture the discriminating features of the 2-D image corresponding to each EEG signal in order to distinguish seizure and non-seizure subjects using Multi-Layer Perceptron (MLP) algorithm. Main Results. The robustness of the introduced CQ-NSGT technique in transforming the 1-D EEG signals into 2-D spectrograms is assessed by comparing its classification results with the Continuous Wavelet Transform (CWT) method, and the results elucidate the high performance of the CQ-NSGT technique. The suggested epileptic seizure classification framework is investigated with clinical EEG data acquired from the Bonn University database, and the experimental results reveal the superior performance of the proposed framework over other state-of-the-art approaches with an accuracy of 99.56 %, sensitivity of 99.12 %, specificity of 99.67 %, and precision of 98.69 %. Significance. This elucidates the importance of the proposed automated system in helping neurologists to accurately interpret and classify epileptic EEG records without necessitating tedious visual inspection or massive data analysis for long-term EEG signals.

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An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

Cited by 11 publications

References 55 publications

Laser gyro signal filtering by combining CEEMDAN and principal component analysis

Laser gyro signal filtering by combining CEEMDAN and principal component analysis

Resolving mode mixing in wheel–rail surface defect detection using EMD based on binary time scale

Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network

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