Heart Sound De-Noising using Wavelet and Empirical Mode Decomposition Based Thresholding Methods

UM > Faculty of Science and Technology > DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING

Status	已發表Published
	Heart Sound De-Noising using Wavelet and Empirical Mode Decomposition Based Thresholding Methods
	Fan, S.; Sekar, B.; Mak, P. U.; Pun, S. H.; Vai, M. I.
	2018-11-01
Source Publication	Proc. of the The 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018)
Pages	1470-1477
Publisher	World Scientific
Abstract	Heart sound de-noising is considered as an important signal pre-processing step in developing computer assisted heart auscultation model. In this paper, we investigate three white noise reduction methods, namely wavelet transform, wavelet packet transform, and empirical mode decomposition for heart sound de-noising. The de-noised signals are evaluated using signal-to-noise ratio and root mean square error. The results show wavelet transform and empirical mode decomposition methods outperform the wavelet packet transform in heart sound de-noising. The wavelet transform method with ‘dmey’ wavelet provides a better result for most of the heart sound records. These three de-noising methods are useful to attenuate the white Gaussian noise. It can provide a high quality signal for further signal processing and classifying the heart sound signal.
Keyword	Heart sound de-noising Wavelet
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	42452
Document Type	Conference paper
Collection	DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Recommended Citation GB/T 7714	Fan, S.,Sekar, B.,Mak, P. U.,et al. Heart Sound De-Noising using Wavelet and Empirical Mode Decomposition Based Thresholding Methods[C]:World Scientific, 2018, 1470-1477.
APA	Fan, S.., Sekar, B.., Mak, P. U.., Pun, S. H.., & Vai, M. I. (2018). Heart Sound De-Noising using Wavelet and Empirical Mode Decomposition Based Thresholding Methods. Proc. of the The 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), 1470-1477.

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