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.

Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation

2010 ◽  
Vol 40-41 ◽  
pp. 140-145
Author(s):  
Ren Di Yang ◽  
Yan Li Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Noise Cancellation ◽  
High Signal ◽  
Ecg Signal ◽  
Adaptive Noise Cancellation ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Adaptive Noise

To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.

scholarly journals Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 11 (5) ◽  
pp. 7536-7541
Author(s):  
W. Mohguen ◽  
S. Bouguezel
Keyword(s):  
Empirical Mode Decomposition ◽  
Additive White Gaussian Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Thresholding Function

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

Intrinsic Pattern Preserving Boundary Treatment Method for Empirical Mode Decomposition

2016 ◽  
Vol 08 (02) ◽  
pp. 1650008
Author(s):  
Donghoh Kim ◽  
Hee-Seok Oh
Keyword(s):  
Empirical Mode Decomposition ◽  
Treatment Method ◽  
Intrinsic Mode Functions ◽  
Boundary Problems ◽  
Second Stage ◽  
Numerical Studies ◽  
Mode Decomposition ◽  
Boundary Treatment ◽  
Predicted Values ◽  
Two Stages

Empirical mode decomposition (EMD) is a procedure that decomposes a signal into so-called intrinsic mode functions (IMFs) according to the levels of local frequency. Due to its robustness to nonlinear and nonstationary signals, EMD has been widely used in various fields. However, EMD suffers from boundary problems severely. In this paper, an efficient method for boundary treatment is proposed. The proposed method consists of two stages. In the first stage, regression models are adapted to reproduce the intrinsic sinusoid pattern of a signal. Based on predicted values, the signal is extended beyond the boundaries in the second stage. Results from numerical studies including simulation study and a noisy signal analysis demonstrate that the proposed method alleviates the boundary problem and hence provides more accurate decomposition results.

ECG Signal In-Band Noise De-Noising Base on EMD

2018 ◽  
Vol 28 (01) ◽  
pp. 1950017 ◽  
Author(s):  
Hui Xiong ◽  
Chunhou Zheng ◽  
Jinzhen Liu ◽  
Limei Song
Keyword(s):  
Mean Squared Error ◽  
Random Permutation ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Squared Error ◽  
Band Noise ◽  
Mode Decomposition ◽  
Interference Frequency ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.

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 An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Shang Tian ◽  
Jin Wang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

scholarly journals Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Muammar Sadrawi ◽  
Wei-Zen Sun ◽  
Matthew Huei-Ming Ma ◽  
Chun-Yi Dai ◽  
Maysam F. Abbod ◽  
...  
Keyword(s):  
Survival Rate ◽  
Cardiopulmonary Resuscitation ◽  
Empirical Mode Decomposition ◽  
Nonlinear Approximation ◽  
Ensemble Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Significant Difference

Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p<0.05). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.

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. 

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