Analysis of multifibre renal sympathetic nerve recordings

Authors:
Dong Li;Yingxiong Jin;Zhuo Yang;Tao Zhang
Affiliations:
Key Lab of Bioactive Materials, Ministry of Education and College of Life Science, Nankai University, Tianjin, P.R. China;Key Lab of Bioactive Materials, Ministry of Education and College of Life Science, Nankai University, Tianjin, P.R. China;College of Medicine Science, Nankai University, Tianjin, P.R. China;Key Lab of Bioactive Materials, Ministry of Education and College of Life Science, Nankai University, Tianjin, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 0

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

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De-noising by soft-thresholding

IEEE Transactions on Information Theory

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Abstract

Multifibre renal sympathetic nerve activity (RSNA) recordings represent a nonlinear dynamic system with high dimensionality. In this paper, an effort has been made to effectively remove noises and reduce the dynamics of the multifibre RSNA signals to a simpler form. For this purpose, an improved cluster method combined with the wavelet-transform-based denoising approach is proposed. The outcomes of the present work show that wavelet denoising approach is a useful tool for analyzing multifibre RSNA in rats. Furthermore, compared to the original algorithm of the cluster method, the improved one reduces some aspects of bias.