脉象作为人体一项基本的生理特征,反映了个体整体性的生理情况,尤其是个体血管与心脏的健康状况.由于脉象诊断自古以来就存在不够定量客观的现实问题,为了客观定量地对脉象这种微弱信号加以分析,本文利用小波变换在时域与频域多级分辨率的特性,对脉象信息进行多小波分离出高频噪声信号以及基线漂移,在此基础上对信号去噪滤波.利用小波变换奇异点突变,提取脉象周期信息作为一类特征值.通过三次样条拟合出单周期脉象波形,提取出脉象搏动微分、积分曲线以及极值点的信息,阐述了它们与脉形特征对应关系,并作为脉形特征值.通过样条建模计算出的特征值为进一步的脉象识别提供了数据信息.%By utilizing the wavelet transform which has an ability to reveal low-frequency signal and high-frequency signal, the power-frequency and baseline wander interference in pulse signal is eliminated. The signal period is detected with the reconstructed detail signal. Every pulse period can be drawn up with spline fitting. The feature points of the denoised signal are detected with the first derivative of the spline fitting function and the integral function. At last we find out the relationship between the feature of the pulse and the physiological significance.
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