Optimum choice of wavelet function and thresholding rule for ECG signal denoising

This paper presents the optimal selection of thresholding rule and wavelet function for denoising an ECG signal. In the proposed work, a comparative study has been carried out using different wavelet functions and thresholding techniques. Thirteen wavelet functions (`db2', `db3', `db4'...

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Bibliographic Details
Published in2015 International Conference on Smart Sensors and Systems (IC-SSS) pp. 1 - 5
Main Authors Niranjana Murthy, H. S., Meenakshi, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
DWT
Electrocardiography
MSE
Noise reduction
PSNR
Signal denoising
Signal to noise ratio
SNR
threshold
Wavelet analysis
Wavelet transforms
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DOI10.1109/SMARTSENS.2015.7873587

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Summary:This paper presents the optimal selection of thresholding rule and wavelet function for denoising an ECG signal. In the proposed work, a comparative study has been carried out using different wavelet functions and thresholding techniques. Thirteen wavelet functions (`db2', `db3', `db4', `db5', `db6', `db8', `sym4', `sym6', `sym8', `coif2', `coif3', `coif4' and `haar') and four thresholding rules (`Rigrsure', `Heursure', `Sqtwolog' and `Minimaxi') are used. The efficacy of the denoising technique is demonstrated with the help of ECG datasets chosen from physiobank database. Three performance measures such as Signal to Noise ratio (SNR), Mean square error (MSE) and Peak signal to noise ratio (PSNR) are used for optimal selection of thresholding rules and wavelet functions in denoising ECG signal. The results of this study exhibits that the best performance of denoising ECG signal is obtained with the `rigrsure' thresholding rule and `coif2' wavelet function based on performance measures SNR, MSE and PSNR.
DOI:10.1109/SMARTSENS.2015.7873587