Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Xiaoli& Li, Chengwei. 2016. Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100208
Modern Language Association (MLA)
Li, Xiaoli& Li, Chengwei. Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1100208
American Medical Association (AMA)
Li, Xiaoli& Li, Chengwei. Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100208
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100208