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Analysis of Multifibre Renal Sympathetic Nerve Recordings

机译:多纤维肾交感神经记录的分析

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摘要

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.
机译:多纤维肾交感神经活动(RSNA)记录代表具有高维的非线性动力系统。在本文中,已经进行了努力以有效地去除噪声并将多光纤RSNA信号的动态降低为更简单的形式。为此,提出了一种结合基于小波变换的去噪方法的改进聚类方法。当前工作的结果表明,小波去噪方法是分析大鼠多纤维RSNA的有用工具。此外,与聚类方法的原始算法相比,改进后的算法减少了偏差的某些方面。

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