Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection.

Xiaoli Li,Chengwei Li

doi:10.1155/2016/8301962

Abstract

Diabetes is a serious threat to human health. Thus, research on noninvasive blood glucose detection has become crucial locally and abroad. Near-infrared transmission spectroscopy has important applications in noninvasive glucose detection. Extracting useful information and selecting appropriate modeling methods can improve the robustness and accuracy of models for predicting blood glucose concentrations. Therefore, an improved signal reconstruction and calibration modeling method is proposed in this study. On the basis of improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and correlative coefficient, the sensitive intrinsic mode functions are selected to reconstruct spectroscopy signals for developing the calibration model using the support vector regression (SVR) method. The radial basis function kernel is selected for SVR, and three parameters, namely, insensitive loss coefficient ε, penalty parameter C, and width coefficient γ, are identified beforehand for the corresponding model. Particle swarm optimization (PSO) is employed to optimize the simultaneous selection of the three parameters. Results of the comparison experiments using PSO-SVR and partial least squares show that the proposed signal reconstitution method is feasible and can eliminate noise in spectroscopy signals. The prediction accuracy of model using PSO-SVR method is also found to be better than that of other methods for near-infrared noninvasive glucose detection.

Highlights

Diabetes is a chronic disease that poses a serious threat to human health
These limitations are resolved with another noise auxiliary algorithm, called CEEMDAN, which is used to achieve an accurate reconstruction of original signals and pure decomposed mode spectra [24]
The R and root mean square error of prediction (RMSEP) values of the calibration model established with the spectrum data, which were reconstructed with improved CEEMDAN in the partial least squares (PLS) and Particle swarm optimization (PSO)-support vector regression (SVR) methods, are better than those of the calibration model with the original spectrum data

Summary

Introduction

Diabetes is a chronic disease that poses a serious threat to human health. According to the International Diabetes Federation (IDF) in 2014, diabetes affects 387 million individuals around the world, and this figure is expected to increase to 592 million in 2035 [1]. EEMD or CEEMD will produce wrong ingredients components, and the IMFs obtained via decomposition may fail to meet the definition of IMF when parameter selection is ineffective These limitations are resolved with another noise auxiliary algorithm, called CEEMDAN, which is used to achieve an accurate reconstruction of original signals and pure decomposed mode spectra [24]. CEEMDAN still has some problems which need to be improved; for example, its modes contain some residual noise, and the signal information shows some spurious modes in the early stages of decomposition [25] To overcome these two issues, the improved CEEMDAN method is applied in this paper to obtain modes with less noise and more physical meaning.

Methods

Experimental Results and Discussion

Near-Infrared Spectrum Experiments

Conclusion