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Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection
المؤلفون المشاركون
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-08-22
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1100208
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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