改进的自适应噪声总体集合经验模态分解在光谱信号去噪中的应用

针对近红外无创血糖检测过程中噪声对血糖浓度模型精度和稳定性的影响,提出用自适应噪声总体集合经验模态分解方法实现近红外光谱信号的去噪;同时,根据原始信号曲率和分解后本征模态函数(IMFs)曲率间的离散弗雷歇距离选择相关模态。首先,将自适应噪声的总体集合经验模态分解方法引入近红外光谱去噪过程,介绍了经验模态分解、集合经验模态分解、互补集合经验模态分解及自适应噪声总体集合经验模态分解的基本原理及具体实现过程。然后,应用基于曲率和离散弗雷歇距离的自适应噪声总体集合经验模态分解改进算法对仿真信号和光谱信号进行去噪,并将其标准差和信噪比作为评价指标。实验结果表明:应用提出的方法得到的血糖浓度近红外光谱数据其标准差为0.179 4,信噪比为19.117 5dB,实现了信号与噪声的分离,改善了重构信号质量,具有良好的自适应性,可以有效识别并提取有用信息。

Application of improved complete ensemble empirical mode decomposition with adaptive noise in spectral signal denoising

As the accuracy and stability of a blood glucose level model is affected by the noise in near infrared non-invasive blood glucose detection process, an improved complete ensemble empirical mode decomposition method with adaptive noise was proposed for denoising of near infrared spectroscopy signals. Meanwhile, a mode selection method based on Frechet distance combining with the feature of curve curvature was proposed for the selection of Intrinsic Mode Functions(IMFs). Firstly. the complete ensemble empirical mode decomposition method with adaptive noise was introduced in the denoising processing of near infrared spectroscopy, and the basic principles and concrete realization processes of empirical mode decomposition, ensemble empirical mode decomposition, complementary ensemble empirical mode decomposition and the complete ensemble empirical mode decomposition based on adaptive noise were described. Then, an improved complete ensemble empirical mode decomposition method with adaptive noise based on curvature and discrete Frechet distance was applied in denoising for simulation signals and spectral signals， and their standard deviation and the Signal to Noise Ratio(SNR) were taken as the evaluation indexes. The simulation and experimental results show that the standard deviation of the improved method based on curvature and discrete Frechet distance in the near infrared spectral signal is 0.179 4, and the SNR is 19.117 5 dB, which extracts useful information, realizes the separation of signal and noise, and improves the quality of reconstructed signals. The proposed method has a good adaptability to effectively identify and separate the signal and noise components.