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.
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