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作 者:张林 余成波 谭拥 米有玉 ZHANG Lin;YU Cheng-bo;TAN Yong;MI You-yu(College of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 401320,China)
机构地区:[1]重庆理工大学电气与电子工程学院,重庆401320
出 处:《计算机仿真》2023年第7期342-347,500,共7页Computer Simulation
基 金:国家自然科学基金资助项目(61976030);斯沃德股份有限公司资助项目(2018Q131)。
摘 要:针对可穿戴血氧检测中运动伪影(Motion Artifact, MA)干扰较大使检测结果不准确的问题,提出了一种基于变分模态分解(Variational Mode Decomposition, VMD)和归一化最小均方误差(Normalized Least Mean Square, NLMS)自适应滤波的算法。首利用VMD算法将光电容积脉搏波信号(Photoplethysmographic, PPG)分解为一系列本征模态分量(Intrinsic Mode Function, IMF);计算各IMF的多尺度排列熵(Multiscale Permutation Entropy, MPE),并根据结果选择合适的IMF进行噪声参考信号的重构;将生成的噪声参考信号与原信号一起输入自适应滤波器,滤除运动伪影。实验结果表明,提出的方法比基于加速度的经验模态分解(Empirical Mode Decomposition, EMD)方法和基于加速度的变分模态分解方法信噪比更高,均方误差更小,使用去噪后的信号计算得到的血氧饱和度值的误差降低了2.0%~3.0%,因此,使用上述方法构造的噪声模型可以更好的用于血氧饱和度的测量。A novel adaptive filtering algorithm based on Variational Mode Decomposition(VMD)and Normalized Least Mean Square(NLMS)is proposed to address the issue of inaccurate detection results caused by motion artifact(MA)interference in wearable blood oxygen detection.Firstly,the Photoplethysmographic signal was decomposed in-to a series of Intrinsic Mode functions using the VMD algorithm.Then,the Multiscale Permutation Entropy of each IMF was calculated,and the appropriate IMF was selected according to the results to reconstruct the noise reference signal.Finally,the generated noise reference signal and the original signal were input into an adaptive filter to filter out the motion artifacts.The experimental results show that the method proposed in this paper has a higher signal-to-noise ra-tio and a smaller mean square error than the acceleration-based Empirical Mode Decomposition method,andthe error of the calculated blood oxygen saturation value using the denoised signal has been reduced by 2.0%to 3.0%.Therefore,the noise model constructed using the above method can be better used for measuring blood oxygensaturation.
分 类 号:TN911.4[电子电信—通信与信息系统]
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