Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

scholarly journals Foetal heart rate assessment by empirical mode decomposition and spectral analysis

2019 ◽  
Vol 5 (1) ◽  
pp. 381-383 ◽  
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier
Keyword(s):  
Heart Rate ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Spectral Information ◽  
P Value ◽  
Foetal Heart Rate ◽  
Foetal Heart ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

AbstractThis paper focuses on studying the time-variant dynamics involved in the foetal heart rate (FHR) response resulting from the autonomic nervous system modulation. It provides a comprehensive analysis of such dynamics by relating the spectral information involved in the FHR signal with foetal physiological characteristics. This approach is based on two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modelling. First, the CEEMDAN allows to decompose the signal into intrinsic mode functions (IMFs). Then, the TV-AR modelling allows to analyse their spectral dynamics. Results reveal that the IMFs can involve significant spectral information (p -value < 0.05) that can help to assess the foetal condition.

Applications of Ceemdan in Dynamic Behavior of Defected Spur Gearbox Running Under Acyclism Regime

2020 ◽  
Vol 36 (6) ◽  
pp. 825-839
Author(s):  
A. Hammami ◽  
A. Hmida ◽  
M. T. Khabou ◽  
F. Chaari ◽  
M. Haddar ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Empirical Mode Decomposition ◽  
Combustion Engine ◽  
Stationary Regime ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Emd Method ◽  
Spur Gearbox

ABSTRACTEmpirical Mode Decomposition (EMD) and its approaches are powerful techniques in signal processing especially for the diagnosis of rotating machinery running in non-stationary regime. We are interested in this paper to the dynamic behavior of a defected one stage gearbox equipped with an elastic coupling and loaded under acyclism regime generated by a combustion engine. Actually, we adopt an approach to the EMD method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) as a technique to perform the diagnosis of the studied system. Since the obtained signals are modulated, all obtained Intrinsic Mode Functions (IMFs) are modulated and are processed and shown by the Wigner-Ville distributions (WVD) as well as the spectrum of their envelope in order to detect defects such as cracked tooth defect in the wheel of the spur gearbox and eccentricity defect in the gear.

scholarly journals A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 23 (3) ◽  
pp. 269-280 ◽  
Author(s):  
S. Sengottuvel ◽  
Pathan Fayaz Khan ◽  
N. Mariyappa ◽  
Rajesh Patel ◽  
S. Saipriya ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Slow Wave ◽  
Cardiac Activity ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Motion Artifacts ◽  
Independent Component ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

scholarly journals A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1039 ◽  
Author(s):  
Haikun Shang ◽  
Yucai Li ◽  
Junyan Xu ◽  
Bing Qi ◽  
Jinliang Yin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Partial Discharge ◽  
Hybrid Approach ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Fusion Algorithm ◽  
Mode Decomposition ◽  
Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

scholarly journals Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 513 ◽  
Author(s):  
Fang Ma ◽  
Liwei Zhan ◽  
Chengwei Li ◽  
Zhenghui Li ◽  
Tingjian Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Superior Performance ◽  
Normal Operation ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Spurious Mode ◽  
Adaptive Noise

Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods

2018 ◽  
Vol 18 (2) ◽  
pp. 347-375 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Moving Average Model ◽  
Adaptive Noise

Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.

scholarly journals The extraction of informative vibration components of bearings by different adaptive methods

2021 ◽  
Author(s):  
Serhii Mykhalkiv
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Adaptive Noise ◽  
Impulse Modulation

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

Empirical mode decomposition and tissue harmonic imaging

2005 ◽  
Vol 31 (8) ◽  
pp. 1051-1061 ◽  
Author(s):  
Michael Bennett ◽  
Steve McLaughlin ◽  
Tom Anderson ◽  
Norman McDicken
Keyword(s):  
Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

Sign in / Sign up

Export Citation Format

Share Document