— Intelligent computer-aided heart sound (HS) auscultation provides quantitative and qualitative HS interpretation for preemptive cardiovascular disease (CVD). However, the noises corruption in electronic stethoscope acquired HS signals will not only pollute the HS pathological characteristics but also deteriorate diagnosis accuracy dramatically. As a consequence, HS denoising plays a pivotal role to obtain qualified HS signals for further analysis and interpretation. Massive pathological information contained in murmurs is vulnerable to be distorted by using traditional wavelet based shrinkage methods. Tackling this, a dynamic threshold wavelet shrinkage (DTWS) method is proposed in this paper. Firstly, characteristic layers containing most HS and murmurs information are identified. Then dynamic threshold shrinkage and traditional shrinkage are conducted on characteristic and noncharacteristic layers respectively. Taking advantage of dynamic thresholds, DTWS could overcome the shortcomings of traditional wavelet shrinkage method and reserve the foremost HS and murmurs information while eliminating noises utmostly. Experiments using HS signals from eGeneral Medical benchmark database validate the high performance of the proposed DTWS method with better denoising results than traditional shrinkage method in terms of both signal-noise ratio (SNR) and root mean square error (RMSE).
— Characteristic layers, denoising, dynamic threshold, heart sound, wavelet shrinkage.
The authors are with the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau S.A.R., China (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Tao Zeng, Jia Li Ma, Bin Bin Fu, and Ming Chui Dong, " An Exploration of Dynamic Threshold Wavelet Shrinkage Method for Heart Sound Denoising," International Journal of Information and Electronics Engineering vol. 5, no. 2, pp. 93-97, 2015.