Discrete wavelet transform (DWT) has been widely adopted in biomedical signal processing for denoising, compression
and so on. Choosing a suitable decomposition level (DL) in DWT is of paramount importance to its performance. In this paper, we propose to exploit sparseness of the transformed signals to determine the appropriate DL. Simulation results have shown that the sparseness of transformed signals after DWT increases with the increasing DLs. Additional Monte-Carlo simulation results have verified the effectiveness of sparseness measure in determining the DL.
<p>[1]S. K. Mitra, Digital Signal Processing: A Computer Based Approach.
McGraw-Hill, 2005.
[2] S. S. Patil and M. K. Pawar, “Quality advancement of EEG by wavelet
denoising for biomedical analysis,” in Proc. Int. Conf. on Communication,
Information and Computing Technology, Oct. 2012.
[3] X. Liu, Y. J. Zheng, M. W. Phyu, B. Zhao, and X. J. Yuan, “Power
& area efficient wavelet-based on-chip ECG processor for WBAN,” in
Proc. IEEE Int. Conf. on Body Sensor Networks, 2010, pp. 124–130.
[4] P. Karthikeyan, M. Murugappan, and S.Yaacob, “ECG signal denoising
using wavelet thresholding techniques in human stress assessment,”
International Journal on Electrical Engineering and Informatics, vol. 4,
no. 2, Jul. 2012.
[5] Z. Zhao and P. Min, “ECG denoising by sparse wavelet shrinkage,” in
Proc. IEEE Conf. Bioinformatics and Biomedical Engineering, 2007,
pp. 786–789.
[6] X. Liu, Y. J. Zheng, M. W. Phyu, B. Zhao, M.-K. Y. Je, and X. J. Yuan,
“Multiple functional ECG signal is processing for wearable applications
for long-term cardiac monitoring,” IEEE Trans. Biomedical Engineering,
vol. 58, pp. 380–389, 2011.
[7] R. Cohen, “Signal denoising using wavelets,” Technion, Israel Institute
of Technology, Tech. Rep., 2011.
[8] D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Information
Theory, vol. 41, pp. 613–627, 1995.
[9] Y. H. Peng, “De-noising by modified soft-thresholding,” in Proc. IEEE
Asia-Pacific Conf. Circuits and Systems, 2000, pp. 760–762.
[10] D. W. Yan-Fang Sang and J.-C. Wu, “Entropy-based method of choosing
the decomposition level in wavelet threshold de-noising,” Journal of
Entropy and Information Studies, Jun. 2010.
[11] J. S. M. D. C. Robertson, O. I. Camps and W. B. Gish, “Wavelets and
electromagnetic power system transient,” IEEE Trans. Power Delivery,
vol. 11-2, pp. 1050–1058, Apr. 1996.
[12] A. M. R. Dixon, G. Allstot, D. Gangopadhyay, and D. J. Allstot,
“Compressed sensing system considerations for ECG and EMG wireless
biosensors,” IEEE Trans. Biomedical Circuits and Systems, vol. 6, no. 2,
pp. 155–166, Apr. 2012.
[13] Online Available: http://gerstner.felk.cvut.cz/biolab/newbiolab/teach
/mitdat.htm</p>
<p>[1]S. K. Mitra, Digital Signal Processing: A Computer Based Approach.
McGraw-Hill, 2005.
[2] S. S. Patil and M. K. Pawar, “Quality advancement of EEG by wavelet
denoising for biomedical analysis,” in Proc. Int. Conf. on Communication,
Information and Computing Technology, Oct. 2012.
[3] X. Liu, Y. J. Zheng, M. W. Phyu, B. Zhao, and X. J. Yuan, “Power
& area efficient wavelet-based on-chip ECG processor for WBAN,” in
Proc. IEEE Int. Conf. on Body Sensor Networks, 2010, pp. 124–130.
[4] P. Karthikeyan, M. Murugappan, and S.Yaacob, “ECG signal denoising
using wavelet thresholding techniques in human stress assessment,”
International Journal on Electrical Engineering and Informatics, vol. 4,
no. 2, Jul. 2012.
[5] Z. Zhao and P. Min, “ECG denoising by sparse wavelet shrinkage,” in
Proc. IEEE Conf. Bioinformatics and Biomedical Engineering, 2007,
pp. 786–789.
[6] X. Liu, Y. J. Zheng, M. W. Phyu, B. Zhao, M.-K. Y. Je, and X. J. Yuan,
“Multiple functional ECG signal is processing for wearable applications
for long-term cardiac monitoring,” IEEE Trans. Biomedical Engineering,
vol. 58, pp. 380–389, 2011.
[7] R. Cohen, “Signal denoising using wavelets,” Technion, Israel Institute
of Technology, Tech. Rep., 2011.
[8] D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Information
Theory, vol. 41, pp. 613–627, 1995.
[9] Y. H. Peng, “De-noising by modified soft-thresholding,” in Proc. IEEE
Asia-Pacific Conf. Circuits and Systems, 2000, pp. 760–762.
[10] D. W. Yan-Fang Sang and J.-C. Wu, “Entropy-based method of choosing
the decomposition level in wavelet threshold de-noising,” Journal of
Entropy and Information Studies, Jun. 2010.
[11] J. S. M. D. C. Robertson, O. I. Camps and W. B. Gish, “Wavelets and
electromagnetic power system transient,” IEEE Trans. Power Delivery,
vol. 11-2, pp. 1050–1058, Apr. 1996.
[12] A. M. R. Dixon, G. Allstot, D. Gangopadhyay, and D. J. Allstot,
“Compressed sensing system considerations for ECG and EMG wireless
biosensors,” IEEE Trans. Biomedical Circuits and Systems, vol. 6, no. 2,
pp. 155–166, Apr. 2012.
[13] Online Available: http://gerstner.felk.cvut.cz/biolab/newbiolab/teach
/mitdat.htm</p>
@article{"International Journal of Electrical, Electronic and Communication Sciences:65056", author = "Lei Lei and Chao Wang and Xin Liu", title = "Discrete Wavelet Transform Decomposition Level Determination Exploiting Sparseness Measurement", abstract = "Discrete wavelet transform (DWT) has been widely adopted in biomedical signal processing for denoising, compression
and so on. Choosing a suitable decomposition level (DL) in DWT is of paramount importance to its performance. In this paper, we propose to exploit sparseness of the transformed signals to determine the appropriate DL. Simulation results have shown that the sparseness of transformed signals after DWT increases with the increasing DLs. Additional Monte-Carlo simulation results have verified the effectiveness of sparseness measure in determining the DL.
", keywords = "Sparseness, DWT, decomposition level, ECG.", volume = "7", number = "9", pages = "1185-4", }
