scholarly journals Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1169
Author(s):  
Mingjing Chen ◽  
Aodi He ◽  
Kaicheng Feng ◽  
Guanzheng Liu ◽  
Qian Wang
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Transfer Entropy ◽  
Intrinsic Mode Functions ◽  
Rr Interval ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Novel Method ◽  
Screening Accuracy

Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

2016 ◽  
Vol 28 (10) ◽  
pp. 3073-3094 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Hamido Fujita ◽  
Vidya K. Sudarshan ◽  
Shu Lih Oh ◽  
Adam Muhammad ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Automated Identification ◽  
Mode Decomposition

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 791-793 ◽  
pp. 1006-1009
Author(s):  
Jia Xing Zhu ◽  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Mode Decomposition ◽  
Novel Method

Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.

The Fault Pattern Identification of Hydraulic Pump Based on the Sensitive Component STSA

2014 ◽  
Vol 635-637 ◽  
pp. 790-794
Author(s):  
Yu Kui Wang ◽  
Hong Ru Li ◽  
Peng Ye
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Novel Method ◽  
Symbolic Time Series Analysis

A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

Denoise PET Images Based on a Combining Method of EMD and ICA

2014 ◽  
Vol 981 ◽  
pp. 340-343
Author(s):  
Qi Wei ◽  
Qi Liu
Keyword(s):  
Positron Emission Tomography ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Independent Component ◽  
Emission Tomography ◽  
Intrinsic Mode Functions ◽  
Positron Emission ◽  
Mode Decomposition ◽  
Novel Method ◽  
Simulation Results

The incidental component in addition to the measured target signals is considered as noise of Positron Emission Tomography (PET) images. A novel method to denoise the PET images based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) associated with Sparse Code Shrinkage (SCS) technique is proposed in this paper. EMD is executed to decompose a PET image into a number of Intrinsic Mode Functions (IMFs), which are used to reconstruct a new PET image after chosen by means of an inverse EMD procedure. By applying ICA to the new PET image, an orthogonal dataset can be obtained and the signal-noise separation can be realized. Then a clearer PET image can be reconstructed by SCS. The simulation results indicate that the proposed method is effective to denoise PET images.

A NOVEL APPROACH OF EDGE DETECTION VIA A FAST AND ADAPTIVE BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION METHOD

2010 ◽  
Vol 02 (02) ◽  
pp. 171-192 ◽  
Author(s):  
SHARIF M. A. BHUIYAN ◽  
JESMIN F. KHAN ◽  
REZA R. ADHAMI
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Morphological Operations ◽  
Intrinsic Mode Functions ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Edge Detectors ◽  
Bidimensional Empirical Mode Decomposition

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

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.

scholarly journals Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 873 ◽  
Author(s):  
Zhe Wu ◽  
Qiang Zhang ◽  
Lixin Wang ◽  
Lifeng Cheng ◽  
Jingbo Zhou
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Rotating Machinery ◽  
Transfer Entropy ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Coupling Characteristics ◽  
High Frequency Harmonic ◽  
Frequency Harmonic

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time–frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method—the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)—is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

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