Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

2018 ◽  
Vol 38 (2) ◽  
pp. 297-312 ◽  
Author(s):  
Shailesh Kumar ◽  
Damodar Panigrahy ◽  
P.K. Sahu
Keyword(s):  
Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mode Decomposition ◽  
Local Mean ◽  
Non Local ◽  
Electrocardiogram Ecg

scholarly journals ECG Noise Cancellation Based on Grey Spectral Noise Estimation

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 798 ◽  
Author(s):  
Shing-Hong Liu ◽  
Cheng-Hsiung Hsieh ◽  
Wenxi Chen ◽  
Tan-Hsu Tan
Keyword(s):  
Empirical Mode Decomposition ◽  
Noise Cancellation ◽  
Noise Estimation ◽  
Body Motion ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Reconstruction Process ◽  
Second Stage ◽  
Mode Decomposition ◽  
Electrocardiogram Ecg

In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

Separating Nonstationary Powerline Interference from ECG Using Empirical Mode Decomposition

2012 ◽  
Vol 155-156 ◽  
pp. 736-740
Author(s):  
Guo Jun Li ◽  
Xiao Jie Hao ◽  
Hui Zhong ◽  
Xiao Na Zhou
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Significant Source ◽  
Ecg Signal ◽  
Time Varying ◽  
Mode Function ◽  
Mode Decomposition ◽  
Simulation Results ◽  
Powerline Interference ◽  
Electrocardiogram Ecg

Powerline interference (PLI) is a significant source of noise in Electrocardiogram (ECG). It often exhibits variations in frequency and amplitude along with relatively lower level than that of ECG signal in battery-operated ECG system, whose separation remains a challenging task. The use of masking signal-aided empirical mode decomposition is presented to deal with this problem in this study. Simulation results show that our method can effectively decompose the time-varying PLI into a single intrinsic mode function (IMF) at various interference levels.

scholarly journals FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF EMPIRICAL MODE DECOMPOSITION ALGORITHM FOR ELECTROCARDIOGRAM PROCESSING

2017 ◽  
Vol 10 (13) ◽  
pp. 77
Author(s):  
Dylan Royce Fernandes ◽  
Suchetha M
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Programmable Gate Array ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Stopping Criterion ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Field Programmable ◽  
Gate Array ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. Therefore, good quality ECG signal is required. Hilbert-Huang transform (HHT) is a method to analyze non-stationary and non-linear signals. Empirical mode decomposition (EMD) is the core of HHT. EMD breaks down signals into smaller number of components. These components form a complete and nearly orthogonal basis for the original signal. This algorithm is implemented on field-programmable gate array using the process of extrema generation, envelope generation, and stopping criterion. 

scholarly journals Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2325 ◽  
Author(s):  
Yong Lv ◽  
Houzhuang Zhang ◽  
Cancan Yi
Keyword(s):  
Convex Optimization ◽  
Empirical Mode Decomposition ◽  
Dimensional Space ◽  
Real Data ◽  
Rolling Bearing ◽  
Low Rank ◽  
Data Matrix ◽  
Mode Decomposition ◽  
Uniform Sample ◽  
Local Mean

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.

scholarly journals Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

2020 ◽  
Vol 206 ◽  
pp. 03019
Author(s):  
Kun Zhao ◽  
Jisheng Ding ◽  
YanFei Sun ◽  
ZhiYuan Hu
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Local Means ◽  
Residual Component ◽  
Mode Decomposition ◽  
Non Local ◽  
Bidimensional Empirical Mode Decomposition

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

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