scholarly journals Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

2016 ◽  
Vol 6 (5) ◽  
pp. 2197 ◽  
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko
Keyword(s):  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Mean Square ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

2016 ◽  
Vol 6 (5) ◽  
pp. 2197 ◽  
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko
Keyword(s):  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Mean Square ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

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.

scholarly journals Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

2020 ◽  
Vol 10 (6) ◽  
pp. 2038 ◽  
Author(s):  
Yanpeng Wang ◽  
Leina Zhao ◽  
Shuqing Li ◽  
Xinyu Wen ◽  
Yang Xiong
Keyword(s):  
Traffic Flow ◽  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Original Data ◽  
Time Varying ◽  
Mean Square ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

scholarly journals Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

2021 ◽  
Vol 12 (1) ◽  
pp. 01-15
Author(s):  
Rui Gong ◽  
Kazunori Hase ◽  
Hajime Ohtsu ◽  
Susumu Ota
Keyword(s):  
Ant Colony Optimization ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Ant Colony ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Snr Improvement ◽  
Mean Square Difference ◽  
Dynamic Filter

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

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