为了降低噪声对人体脉搏信号的干扰、提高采集精度,提出了一种改进的滤波算法. 从脉搏信号及其噪声特点出发,采用与经验模态分解法结合的方法,选择适当的小波基并改进小波阈值函数,构造模态系数对脉搏信号进行滤波. 经过理论分析与实验验证,取得了理想的实验数据. 结果表明,改进的阈值算法不仅克服了软、硬阈值的局限性,并能有效克服傅里叶变换后产生的边缘效应问题;同时,与经验模态分解法相结合,削弱了低频噪声滤除的误差,增强了小波变换的自适应性,较传统的滤波方法能更好地抑制噪声,有助于提高信噪比.%In order to reduce the disturbance of noise on human sphygmus signal and improve the precision of data acquisition, a new improved filtering method was proposed .Based on the characteristics of sphygmus signal and noise , by using empirical mode decomposition method , the appropriate wavelet basis was selected , the wavelet threshold function was improved and a modal coefficients was built suitable for the filtering of a sphygmus signal .And then, sphygmus signals were filtered by the structural modal coefficient .After theoretical analysis and experimental verification , ideal experimental data was obtained .The results show that the improved threshold algorithm not only overcomes the limitations of soft threshold and hard threshold , but also overcomes the edge effect problems effectively which is generated by Fourier transform .At the same time, combining with the empirical mode decomposition method , the filtering errors of low frequency noise are weakened and the adaptability of wavelet transform is enhanced .Compared with the traditional filtering methods , the new method can suppress noise effectively and help to improve the signal-to-noise ratio.
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