The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform
-
Mohamed Rouis
,
Salim Sbaa
Abstract
The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.
Author statement
Research funding: Authors state no funding involved.
Competing interests: The authors declare there are no competing interests.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animal use.
References
[1] Gradolewski D, Redlarski G. Wavelet-based denoising method for real PCG signal recorded by mobile devices in noisy environment. J Comput Biol Med 2014;52:119–29.10.1016/j.compbiomed.2014.06.011Search in Google Scholar
[2] Esra S, Akan A. Heart sound reduction in lung sounds by spectrogram. IFMBE Proc 2005;11:1727–983.Search in Google Scholar
[3] Mallat S. Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 1989;11:674–93.10.1515/9781400827268.494Search in Google Scholar
[4] Feng L, Wang Yu, Wang Ya. Research and implementation of heart sound denoising. Phys Procedia 2012;25:777–85.10.1016/j.phpro.2012.03.157Search in Google Scholar
[5] Castro A, Vinhoza TT, Mattos SS, Coimbra MT. Heart sound segmentation of pediatric auscultations using wavelet analysis. Conf Proc IEEE Eng Med Biol Soc 2013;2013:3909–12.10.1109/EMBC.2013.6610399Search in Google Scholar
[6] Rouis M, Ouafi A, Sbaa S. Optimal level and order detection in wavelet decomposition. Biomed Eng Biomed Tech 2018;63:1–14.Search in Google Scholar
[7] Chourasia VS, Tiwari AK. Design methodology of a new wavelet basis function for fetal phonocardiographic signals. Sci World J 2013;2013:12, ID 505840.10.1155/2013/505840Search in Google Scholar
[8] Naseri H, Homaeinezhad MR, Pourkhajeh H. Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval. Comput Biol Med 2013;43:1205–13.10.1016/j.compbiomed.2013.05.020Search in Google Scholar
[9] eGeneral Medical. Heart murmur database 2010. Available from: http://www.egeneralmedical.com/litohearmur.html. Accessed: January 2015.Search in Google Scholar
[10] Debbal SM, Bereksi-Reguig F. Computerized heart sounds analysis. Comput Biol Med 2008;38:263–80.10.5772/23700Search in Google Scholar
[11] Kwiatkowski D, Phillips P, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root. J Econometr 1992;4:159–78.10.1016/0304-4076(92)90104-YSearch in Google Scholar
[12] Dickey D, Fuller W. Distribution of the estimators for autoregressive time series with a unit root. J Am Statist Assoc 1979;74:427–31.10.1080/01621459.1979.10482531Search in Google Scholar
[13] Beran J, Feng Y, Ghosh S, Kulik R. Long-memory processes: probabilistic properties and statistical methods. New York: Springer; 2013.10.1007/978-3-642-35512-7Search in Google Scholar
[14] Donoho D. De-noising by soft-thresholding. IEEE Trans Inform Theory 1995;41:613–27.10.1109/18.382009Search in Google Scholar
[15] Aggarwal R, Singh JK, Gupta VK, Rathore S, Tiwari M, Khare A. Noise reduction of speech signal using wavelet transform with modified universal threshold. Int J Comput Appl 2011;20:14–9.10.5120/2431-3269Search in Google Scholar
[16] Patil R. Noise reduction using wavelet transform and singular vector decomposition. Procedia Comput Sci 2015;54:849–53.10.1016/j.procs.2015.06.099Search in Google Scholar
[17] Donoho DL, Johnstone IM. Ideal spatial adaptation via wavelet shrinkage. Biometrika 1994;81:425–55.10.1093/biomet/81.3.425Search in Google Scholar
[18] Dokur Z, Ölmez T. Heart sound classification using wavelet transform (TW) and incremental self-organizing map. Science Direct. J. Digital Signal Process 2008;18:951–9.10.1016/j.dsp.2008.06.001Search in Google Scholar
[19] Naseri H, Homaeinezhad MR. Computerized quality assessment of phonocardiogram signal measurement acquisition parameters. J Med Eng Technol 2012;36:308–18.10.3109/03091902.2012.684832Search in Google Scholar PubMed
[20] Daubechies I. Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 1998;41:909–96.10.1137/1.9781611970104.ch6Search in Google Scholar
[21] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13:600–12.10.1109/TIP.2003.819861Search in Google Scholar
[22] Salman AH, Ahmadi N, Mengko R, Langi AZ, Mengko T. Empirical mode decomposition (EMD) based denoising method for heart sound signal and its performance analysis. Int J Electr Comput Eng 2016;6:2197–204.10.11591/ijece.v6i5.11344Search in Google Scholar
[23] Cheng X, Zhang Z. Denoising method of heart sound signals based on self-construct heart sound wavelet. Aip Advances 2014;4:087108.10.1063/1.4891822Search in Google Scholar
[24] Abhishek M, Sinha GR. Denoising of PCG signal by using wavelet transforms. Adv Comput Res 2012;4:46–9.Search in Google Scholar
[25] Abhishek M, Sinha GR, Potdar RM, Kowar MK. Comparison of wavelet transforms for denoising and analysis of PCG signal. I-Manager’s J Communication Eng Syst 2011;1:48–52.Search in Google Scholar
[26] Nabih Ali M, EL-Sayed A El-Dahshan, Ashraf Y. Denoising of heart sound signals using discrete wavelet transform. Circ Syst Signal Process 2017;36:4482–97.10.1007/s00034-017-0524-7Search in Google Scholar
[27] Misal A, Sinha GR. Separation of lung sound from PCG signals using wavelet transform. J Basic Appl Phys 2012;1: 57–61.Search in Google Scholar
©2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- Non-invasive drug delivery technology: development and current status of transdermal drug delivery devices, techniques and biomedical applications
- Research Articles
- Mussel-inspired polydopamine-mediated surface modification of freeze-cast poly (ε-caprolactone) scaffolds for bone tissue engineering applications
- Thermography and colour Doppler ultrasound: a potential complementary diagnostic tool in evaluation of rheumatoid arthritis in the knee region
- Effective segmentation and classification of tumor on liver MRI and CT images using multi-kernel K-means clustering
- GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix
- Monitoring the dynamics of acute radiofrequency ablation lesion formation in thin-walled atria – a simultaneous optical and electrical mapping study
- Dynamic cerebral perfusion parameters and magnetic nanoparticle accumulation assessed by AC biosusceptometry
- The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform
- Correlational study of the center of pressure measures of postural steadiness on five different standing tasks in overweight adults
Articles in the same Issue
- Frontmatter
- Review
- Non-invasive drug delivery technology: development and current status of transdermal drug delivery devices, techniques and biomedical applications
- Research Articles
- Mussel-inspired polydopamine-mediated surface modification of freeze-cast poly (ε-caprolactone) scaffolds for bone tissue engineering applications
- Thermography and colour Doppler ultrasound: a potential complementary diagnostic tool in evaluation of rheumatoid arthritis in the knee region
- Effective segmentation and classification of tumor on liver MRI and CT images using multi-kernel K-means clustering
- GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix
- Monitoring the dynamics of acute radiofrequency ablation lesion formation in thin-walled atria – a simultaneous optical and electrical mapping study
- Dynamic cerebral perfusion parameters and magnetic nanoparticle accumulation assessed by AC biosusceptometry
- The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform
- Correlational study of the center of pressure measures of postural steadiness on five different standing tasks in overweight adults
