Comparison of Wavelet Types and Thresholding Methods on Wavelet Based Denoising of Heart Sounds

Burhan Ergen

Department of Computer Engineering, Firat University, Elazig, Turkey.

DOI: 10.4236/jsip.2013.43B029                

Abstract

This paper focuses on the denoising of phonocardiogram (PCG) signals by means of discrete wavelet transform (DWT) using different wavelets and noise level estimation methods. The signal obtained by denoising from PCG signal contaminated white noise and the original PCG signal is compared to determine the appropriate parameters for denoising. The comparison is evaluated in terms of signal to noise ratio (SNR) before and after denoising. The results showed that the decomposition level is the most important parameter determining the denoising quality.

 

Keywords

Discrete Wavelet Transform; Denoising; PCG

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	M. Akay, J. L. Semmlow, W. Welkowitz, M.D. Bauer and J. B. Kostis, “Detection of Coronnary Occlusion Using Autoregressive Modeling Diastolic Heart Sounds,” IEEE Transactions on Biomedical Engineering, Vol. 37, 1990, pp. 336-273.doi:10.1109/10.52343
[2]	L.G. Durand, M. Blanchard, G. Cloutier, H. N. Sabbah and P.D. Stein, “Comparison of Pattern Recognition Methods for Computer-Assisted Classification of Spectra of Heart Sounds in Patient with Porcine Bioprosthetic Valve Implanted in the Mitral Position,” IEEE Transactions on Biomedical Engineering, Vol. 37, 1990, pp. 1121-1129.doi:10.1109/10.64456
[3]	B. Ergen and Y. Tatar, “The Analysis of Heart Sounds Based on Linear and High Order Statistical Methods,” Proceedings of the 23rd Annual EMBS International Conferences, 2001, pp. 2139-2141.
[4]	P. E. Tikkanen, “Nonlinear Wavelet and Wavelet Packet Denoising of Electrocardiogram Signal,” Biological Cybernetics, Springer-Verlag, 1999.
[5]	S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Anal. And Mach. Int., Vol. 11, No. 7, 1989, pp. 674-693.
[6]	O. Rioul, “Discrete-Time Multiresolution Theory,” IEEE Trans. on Signal Processing, Vol. 41, No. 8, 1993, pp. 2591-2605. doi:10.1109/78.229891
[7]	E. Erçelebi, “Electrocardiogram Signals Denoising Using Lifting Based Discrete Wavelet Transform,” Comp. in Biol. and Med., Vol. 34, 2004, pp. 479-493. doi:10.1016/S0010-4825(03)00090-8
[8]	H. F. C-Greiff, R. R-Garcia and J. V. L-Ginori, “Signal Denoising in Magnetic Resonance Spectroscopy Using Wavelet Transforms,” Vol.14, No. 6, 2002, pp. 388-401.
[9]	A. Effern, K. Lehnertz, T. Schreiber, T. Grundwald, P. David and C. E. Elger, “Nonlinear Denoising of Transient Signals with Application to Event-related Potentials,” Physica, Vol. 140, 2000, pp. 257-266.