头部动态场景下非接触式血氧饱和度测量

头部动态场景下非接触式血氧饱和度测量

血氧饱和度是评估人体健康状况的重要生理参数之一，传统的接触式血氧饱和度测量方法存在诸多不便，如需要与皮肤直接接触、使用一次性传感器等。随着计算机视觉和信号处理技术的发展，非接触式血氧饱和度测量技术逐渐成为研究热点。然而，现有的非接触式测量方法在头部动态场景下准确性较低，难以满足实际应用需求。本文提出了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，以解决这一问题。

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。

图表说明

图 1 Hb和HbO2吸收光谱
Figure 1. Absorption spectra of Hb and HbO2

图 2 基于ICEEMDAN-WT的血氧饱和度测量整体设计图
Figure 2. Overall design of blood oxygen saturation measurement based on ICEEMDAN-WT

图 3 检测追踪效果图
Figure 3. Detection and tracking effect

图 4 皮肤分割效果图
Figure 4. Skin segmentation effect

图 5 像素平均后B通道和R通道信号
Figure 5. B channel and R channel signals after pixel average

图 6 去直流后B通道和R通道信号
Figure 6. B channel and R channel signals after removing DC

图 7 B通道分解后的信号
Figure 7. B channel decomposed signal

图 8 B通道对应的频谱分量
Figure 8. Spectral components of each mode of the B channel

图 9 R通道分解后的信号
Figure 9. R channel decomposed signal

图 10 R通道对应的频谱分量
Figure 10. Spectral components of each mode of the R channel

图 11 B通道重构后的信号
Figure 11. Reconstructed signal of channel B

图 12 R通道重构后的信号
Figure 12. Reconstructed signal of channel R

图 13 头部运动部分帧
Figure 13. Partial head movement frames

图 14 评价指标对比
Figure 14. Evaluation index comparison

图 15 不同方法的MAE对比
Figure 15. MAE comparison of different methods

图 16 Bland-Altman散点图
Figure 16. Bland-Altman scatter plot

表格数据

表 1 不同运动场景之下的SpO2结果
Table 1. Blood oxygen results under different exercise scenarios(%)

表 2 不同运动场景下算法性能对比
Table 2. Comparison of algorithm performances in different motion scenes (Unit: %)

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