scholarly journals Research on Heart Sound Signal Denoising Algorithm Based on Variational Mode Decomposition and Wavelet Threshold

2021 ◽  
Vol 09 (10) ◽  
pp. 110-121
Author(s):  
Qian Liu ◽  
Yan Xu ◽  
Lianjun Zhang ◽  
Chunyan Liang
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko

<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>


Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko

<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>


2014 ◽  
Vol 484-485 ◽  
pp. 396-399
Author(s):  
Le Juan Zhang ◽  
Xing Hai Yang ◽  
Nian Qiang Li ◽  
Shi Yao Cui

In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated filtering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved.


2021 ◽  
Vol 179 ◽  
pp. 260-267
Author(s):  
Norezmi Jamal ◽  
Nabilah Ibrahim ◽  
MNAH Sha’abani ◽  
Farhanahani Mahmud ◽  
N. Fuad

Author(s):  
Madhwendra Nath ◽  
Subodh Srivastava ◽  
Niharika Kulshrestha ◽  
Dilbag Singh

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.


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