头部动态场景下非接触式血氧饱和度测量

刘涛; 张亚莉

doi:10.37188/CO.2024-0034

头部动态场景下非接触式血氧饱和度测量

doi: 10.37188/CO.2024-0034

刘涛^{1, 2,},
张亚莉^1, ,

1.
西安科技大学通信与信息工程学院, 陕西西安 710600
2.
西安市网络融合通信重点实验室, 陕西西安 710000

基金项目: 国家重点研发计划项目（2018YFC0808），陕西省重点研发项目（2019SF-260）

详细信息

作者简介:
刘涛(1972—)，男，陕西西安人，博士，副教授，硕士生导师，2009 年于西安科技大学获得博士学位，主要从事数字信号处理、物联网系统、网络安全方面的研究。E-mail：liutao@xust.edu.cn

张亚莉(1996—)，女，陕西宝鸡人，硕士研究生，2021 年于陕西理工大学获得学士学位，主要从事医学信号及数字信号处理方面的研究。E-mail：3176207132@qq.com

中图分类号: TN911;TP3391
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出版历程
- 收稿日期: 2024-02-07
- 录用日期: 2024-04-22
- 网络出版日期: 2024-05-10

Non-contact blood oxygenin saturation measurement dynamic head scenarios

LIU Tao^{1, 2
,},
ZHANG Ya-li^{1
, ,}

1.
School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710600, China
2.
Xi’an Key Laboratory of Network Fusion Communication, Xi’an 710600, China

Funds: Funded by National Key Research and Development Program of China (2018YFC0808), Shaanxi Province Key Research and Development Project (2019SF-260)

More Information

Corresponding author: 18740772907@163.com

摘要

摘要:
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残余噪声问题。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。
- 非接触式 /
- 血氧饱和度 /
- 头部运动 /
- 小波阈值 /
- 分解和重构
Abstract:
Aiming at the low accuracy of existing non-contact blood oxygen saturation measurement methods in dynamic head scenes, a denoising method based on improved adaptive noise complete set empirical mode decomposition and wavelet threshold is proposed to extract pulse wave signals with 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 ICEEMDAN (ICEEMDAN), so as to reduce the residual noise in the modal components. Then, ICEEMDAN was used for mode decomposition of pulse wave signals of red and blue channels, and db8 wavelet basis function was used for 3-stage decomposition and reconstruction of components conforming to the spectrum range of blood oxygen, and the reconstructed signals were 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.
- non-contact /
- oxygen saturation /
- head movement /
- wavelet threshold /
- decomposition and reconstruction

HTML全文

图 1 Hb和HbO₂吸收光谱

Figure 1. Hb and HbO2 ofabsorption spectra

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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

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图 3 检测追踪效果图

Figure 3. Detection and tracking effect

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图 4 皮肤分割效果图

Figure 4. Skin segmentation effect

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图 5 像素平均后B通道和R通道信号

Figure 5. Pixels average after B channel and R channel signals

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图 6 去直流后B通道和R通道信号

Figure 6. B channel and R channel signal after DC

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图 7 B通道分解后的信号

Figure 7. B channel decomposed signal

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图 8 B通道对应的频谱分量

Figure 8. Spectral components of each mode of the B channel

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图 9 R通道分解后的信号

Figure 9. R channel decomposed signal

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图 10 R通道对应的频谱分量

Figure 10. Spectral components of each mode of the R channel

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图 11 B通道重构后的信号

Figure 11. Reconstructed signal of channel B

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图 12 R通道重构后的信号

Figure 12. Reconstructed signal of channel R

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图 13 头部运动部分帧

Figure 13. Head movement part of frame

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图 14 评价指标对比

Figure 14. Evaluation index comparison

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图 15 不同方法的MAE对比

Figure 15. Comparison of MAE different methods

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图 16 Bland-Altman散点图

Figure 16. Bland-Altman scatter plot

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表 1 不同运动场景之下的SpO₂结果

Table 1. Blood oxygen results under different exercise scenarios

实验场景	Me(%)	MAE(%)	RMSE(%)
静态场景	0.57	0.64	0.86
说话场景	0.69	0.83	1.08
左右晃动	0.89	0.89	1.26
上下晃动	0.76	1.04	1.29

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表 2 不同运动场景下算法性能对比单位%

Table 2. Performance comparison of algorithms in different motion scenarios Unit: %

方法	静态场景			说话场景			上下晃动场景			左右晃动场景
方法	Me	MAE	RMSE	Me	MAE	RMSE	Me	MAE	RMSE	Me	MAE	RMSE
文献[7]	1.55	2.27	2.74	2.23	1.82	2.88	3.28	2.81	4.32	3.56	2.6	4.41
文献[8]	0.70	1.10	1.30	1.13	1.30	1.72	1.33	1.40	1.93	1.70	1.80	2.23
文献[10]	0.51	1.12	1.23	0.63	0.93	1.12	1.11	1.36	1.75	1.27	1.44	1.92
本文方法	0.57	0.64	0.86	0.69	0.83	1.08	0.89	0.89	1.26	0.76	1.04	1.29

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