基于小波阈值法的脉搏波去噪算法

目的消除可穿戴式脉搏波监测设备在连续测量中由于运动造成的运动伪差,保证设备准确性和稳定性。方法通过选取合适的小波基、小波最大分解层数、阈值函数和阈值方法,对脉搏波信号进行小波阈值处理,提出了一种基于小波阈值法去除脉搏波噪声的算法。并针对在脉搏波信号采集过程中出现的基线漂移、工频干扰和运动伪差,与加窗傅里叶变换去噪后的结果进行对比。结果在信噪比、均方差和平滑度等关键指标上,小波阈值法的效果更优。利用db9小波基对脉搏波信号进行6层小波分解,设置启发式阈值所得到的处理效果最好。结论该算法能够有效抑制工频干扰和运动干扰,使信噪比提高22 dB,均方差接近于0,且平滑度降为原来的11%,实现脉搏波信号采集中干扰的有效去除。

Pulse wave denoising algorithm based on the wavelet threshold method

Objective To eliminate the error caused by motion in the continuous measurement of the wearable pulse wave monitoring device so that the accuracy and stability of the device can be ensured.Methods By choosing the suitable wavelet base,the biggest wavelet decomposition layer,the threshold function and threshold method,we present an algorithm based on the wavelet threshold method to remove pulse wave noise and compare the results of windowed Fourier transform denoising with the baseline drift interference and motion error in pulse wave signal acquisition.Results The wavelet threshold method is more effective in key indicators such as the signal-to-noise ratio,mean square deviation and smoothness.In addition,after using DB9 wavelet base to decompose pulse wave signal with a 6-level wavelet and setting the heuristic threshold,we find that the processing effect is the best.Conclusions This algorithm can effectively suppress power frequency interference and motion interference so that the signal-to-noise ratio will be increased by 22dB,the mean square deviation will get close to 0,and the smoothness will increase to the original 11%,which will realize the effective removal of interference in the pulse wave signal acquisition.