为了研究脉搏波信号降噪的问题, 文章提出了一种集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)与小波阈值相结合的降噪方法, 对采集到的光电容积脉搏波信号来做降噪处理, 同时和EMD结合小波阈值降噪算法进行比较。算法首先把信号做EEMD的分解, 将原始信号分解为n个模态分量(Intrinsic Mode Function, IMF), 然后对这些分量做相干性的计算, 对其中的噪声分量来做小波阈值降噪, 最后将信号重构。原始信号在STM32平台上采用MAX30100传感器测得。实验结果表明: 本文的方法能够很好地剔除光电容积脉搏波中包含高频噪声与基线漂移的各种噪声, 降噪后信噪比为34.09, 均方根误差为1.99。提高了PPG信号的质量, 为光电容积脉搏波信号的准确测量提供了新的思路。

Signals of interest can be affected by various types of noise during the acquisition of photoplethysmography data. To address this problem, a denoising method based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and wavelet threshold was proposed to reduce the noise associated with photoplethysmography signals. In this investigation, this approach was compared with EMD combined with wavelet denoising. Initially, an algorithm applied EEMD to the signal, which was decomposed into a limited number of Intrinsic Mode Functions (IMF). Then, it performed a correlation calculation on the components, followed by wavelet threshold denoising on the noise-containing components. Finally, the signal was reconstructed. The original signal was measured using the stm32 platform with a MAX30100 sensor. The experimental results show that the method can effectively remove high-frequency noise and baseline drift in photoplethysmography. After noise reduction, the signal-to-noise ratio is 34.09 and the root mean square error is 1.99, which improved the signal quality. This new approach facilitates accurate monitoring of photoelectric volume pulse wave signals.