头部动态场景下非接触式血氧饱和度测量

Non-contact blood oxygen saturation measurement in dynamic head scenes

针对现有非接触式血氧饱和度测量方法在头部动态场景下准确性低的问题,提出一种基于改进的自适应噪声完全集合经验模态分解与小波阈值相结合的去噪方法,用于提取高信噪比的脉搏波信号。首先,为解决自适应噪声完全经验模态分解在分解重构早期产生虚假分量和模态混叠的问题,在分解过程中加入高斯白噪声,使其成为改进的自适应噪声完全集合经验模态分解(ICEEMDAN),从而减少模态分量中的残余噪声。然后,使用ICEEMDAN对红蓝色通道的脉搏波信号进行模态分解,并使用db8小波基函数对符合血氧频谱范围的分量进行3级分解和重构,将重构后的信号用于后续血氧值的计算。最后,将不同头部动态场景下测量的血氧饱和度结果进行实验对比分析。结果表明:不同头部场景下得到的血氧饱和度平均误差为0.73%,相较于其他算法平均误差降低1.93%。本文提出的去噪方法在不同头部场景下具有较好的稳定性,可满足日常血氧饱和度测量的需求。

In dynamic head scenes,current non-contact blood oxygen saturation measurement methods have low accuracy.To solve this problem,we propose a denoising method based on improved adaptive noise complete set empirical mode decomposition and wavelet threshold.This method aims to extract pulse wave signals with a high signal-to-noise ratio.Firstly,in order to solve the problem of false components and mode aliasing in the early stage of decomposition and reconstruction,white Gaussian noise is added to the decomposition process to make it become an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),to reduce the residual noise in the modal components.Then,ICEEMDAN is used to perform mode decomposition of pulse wave signals of red and blue channels.The db8 wavelet basis function is used to perform 3-stage decomposition and reconstruction on components within the blood oxygen spectrum range.The reconstructed signals are used for subsequent calculation of blood oxygen value.Finally,the experimental comparison and analysis of the blood oxygen saturation results measured in different dynamic head scenes show that the average error of blood oxygen saturation obtained in different head scenes is 0.73%,which is 1.93%lower than the average error of other algorithms.The denoising method proposed in this paper has good stability in different head scenes and can meet the needs of daily blood oxygen saturation measurement.