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

李晓莉; 李成伟

doi:10.3788/OPE.20162407.1754

您当前的位置：

首页 >

文章列表页 >

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

信息科学 | 更新时间：2020-08-13

- 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
- Application of improved complete ensemble empirical mode decomposition with adaptive noise in spectral signal denoising
- 光学精密工程 2016年24卷第7期页码：1754-1762
- 作者机构：
  
  哈尔滨工业大学电气工程及自动化学院, 黑龙江哈尔滨 150001
- 作者简介：
  
  [ "李晓莉(1984-),女,黑龙江哈尔滨人,博士研究生,2008年、2011年于东北农业大学分别获得学士、硕士学位,主要从事无创血糖检测及胰岛素注射剂量调节研究。E-mail:xiaoli72460@163.com" ]
  李成伟(1963-),男,黑龙江哈尔滨人,博士,教授,1985年、1988年、2000年于哈尔滨工业大学分别获得学士、硕士、博士学位,主要从事生物医学工程、智能控制技术研究。E-mail:lcw@hit.edu.cn E-mail:lcw@hit.edu.cn
- 基金信息：
  
  哈尔滨市科技创新人才专项资金资助项目(2014RFXXJ065);哈尔滨工业大学理工医交叉学科基础研究培育计划资助项目(HIT.IBRSEM.201307)
- DOI：10.3788/OPE.20162407.1754
  中图分类号： O657.33;R587.1
- 收稿日期：2016-03-10，
  
  录用日期：2016-4-25，
  
  纸质出版日期：2016-07
- 稿件说明：
移动端阅览
李晓莉, 李成伟. 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[J]. 光学精密工程, 2016,24(7):1754-1762.

Xiao-li LI, Cheng-wei LI. Application of improved complete ensemble empirical mode decomposition with adaptive noise in spectral signal denoising[J]. Optics and precision engineering, 2016, 24(7): 1754-1762.
李晓莉, 李成伟. 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[J]. 光学精密工程, 2016,24(7):1754-1762. DOI： 10.3788/OPE.20162407.1754.

Xiao-li LI, Cheng-wei LI. Application of improved complete ensemble empirical mode decomposition with adaptive noise in spectral signal denoising[J]. Optics and precision engineering, 2016, 24(7): 1754-1762. DOI： 10.3788/OPE.20162407.1754.

摘要

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0.1794，信噪比为19.1175 dB，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。

Abstract

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.

关键词

Keywords

references

do AMARAL C E F, WOLF B. Current development in non-invasive glucose monitoring[J].Medical. Engineering & Physics, 2008,30(5):541-549.

DU NCAN A, HANNIGAN J, FREEBORN S S, et al..A portable non-invasive blood glucose monitor[C]. The 8 th International Conference on Solid-State Sensors and Actuators, 1995, 2:455-458.

BLANCO J R, FERRERO F J, CAMPO J C, et al.. Design of a low-cost portable potentiostat for amperometric biosensors[C]. 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings, 2006:690-694.

UNNIKRISHNA K A, HEMACHANDRAN D, ABHISHEK T K. A survey on non-invasive blood glucose monitoring using NIR[C].2013 International Conference on Communications and Signal Processing(ICCSP), 2013:1069-1072.

周玲芳, 陈菲. 基于斜率差值的自适应图像椒盐噪声滤波算法[J]. 液晶与显示, 2015, 30(4):695-700.

ZHOU L F, CHEN F. Adaptive slope difference algorithm for filtering salt and pepper noise in image[J]. Chinese Journal of Liquid Crystals and Displays, 2015,30(4):695-700.(in Chinese)

董雪,林志贤, 郭太良. 基于LoG算子改进的自适应阈值小波去噪算法[J]. 液晶与显示, 2014, 29(2):275-280.

DONG X, LIN ZH X, GUO T L. Improved self-adaptive threshold wavelet denoising analysis based on LoG operator[J].Chinese Journal of Liquid Crystals and Displays, 2014,29(2):275-280.(in Chinese)

李权, 赵勋杰, 彭青艳,等. 基于主成分分析法的窗口自适应粒子滤波算法[J]. 红外与激光工程, 2014, 43(10):3474-3479.

LI Q, ZHAO X J, PENG Q Y, et al.. Windows adaptive particle filter algorithm based on principal component analysis[J].Infrared and Laser Engineering, 2014, 43(10):3474-3479.(in Chinese)

顾有林, 叶应流, 曹光华,等. EMD和小波变换在低可探测目标检测中的应用[J]. 红外与激光工程, 2015, 44(11):3494-3499.

GU Y L, YE Y L, CAO G H, et al.. Application of EMD and wavelet transform in low detectable targets detection[J].Infrared and Laser Engineering, 2015,44(11):3494-3499.(in Chinese)

HUANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond. A,1998, 454:903-995.

李欣,梅德庆,陈子辰. 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取[J]. 光学精密工程, 2011,19(6):1291-1297.

LI X,MEI D Q,CHEN Z CH.Feature extraction of chatter for precision hole boring processing based on EMD and HHT[J].Opt. Precision Eng., 2011,19(6):1291-1297.(in Chinese)

REN Y, SUGANTHAN P N, SRIKANTH N. A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods[J]. IEEE Transactions on Sustainable Energy 2015, 6(1):236-244.

鲁丽,颜国正,赵凯,等. 基于集合经验模态分解的人体结肠动力分析[J]. 光学精密工程, 2015,23(6):1850-1856.

LU L, YAN G ZH, ZHAO K, et al.. Analysis of human colonic motility using EEMD[J]. Opt. Precision Eng., 2015,23(6):1850-1856.(in Chinese)

GAN Y, SUI L, WU J, et al.. An EMD threshold denoising method for inertial sensors[J]. Measurement,2014, 49(1), 34-41.

罗玉昆,罗诗途,罗飞路,等. 激光超声信号去噪的经验模态分解实现及改进[J]. 光学精密工程, 2013,21(2):479-487.

LUO Y K, LUO SH T, LUO F L,et al.. Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition[J]. Opt. Precision Eng., 2013,21(2):479-487.(in Chinese)

MARIA E T, MARCELO A C, GASTON S, et al.. A complete ensemble empirical mode decomposition with adaptive noise[C]. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 2011:4144-4147.

JIA R Y, JIANN S S. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis, 2010, 2(2):135-156.

ANNE H H, PIERRE A, GUILLAUME M,Analysis of laser speckle contrast images variability using a novel empirical mode decomposition:comparison of results with laser Doppler flowmetry signal variability[J]. IEEE Transactions on Medical Imaging, 2015, 34(2):618-626.

COOLIDGE J L. The unsatisfactory story of curvature[J].The American Mathematical Monthly, 1952, 59(6):375-379.

R S, KARTHIK K, CHIRANJIB B. Frechet distance based Approach for searching online handwritten documents[C]. Ninth International Conference on Document Analysis and Recognition,(ICDAR),2007.

HAN L, LI C, LIU H. Feature extraction method of rolling bearing fault signal based on EEMD and cloud model characteristic entropy[J]. Entropy, 2015, 17(10):6683-6697.

LI C, ZHAN L. A hybrid filtering method based on a novel empirical mode decomposition for friction signals[J]. Measurement Science & Technology, 2015, 26(12):125003.

浏览量

364

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

层次化点云去噪算法

壳段厚度激光检测信号的变分模态分解去噪

激光超声信号去噪的经验模态分解实现及改进

棉-涤混纺面料中棉含量的近红外光谱分析

隐形眼镜投影测量仪的设计