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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 8301962, 9 pages
http://dx.doi.org/10.1155/2016/8301962
Research Article

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

School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Received 14 April 2016; Revised 18 July 2016; Accepted 27 July 2016

Academic Editor: Thomas Desaive

Copyright © 2016 Xiaoli Li and Chengwei Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 , penalty parameter , and width coefficient , 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.