Non-Invasive In-Vivo Blood Glucose Levels Prediction Using Near Infrared Spectroscopy

2004 ◽  
Author(s):  
C. Araujo-Andrade
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels

In Vivo Near-Infrared Spectroscopy of Rat Skin Tissue with Varying Blood Glucose Levels

2006 ◽  
Vol 78 (1) ◽  
pp. 215-223 ◽  
Author(s):  
Jonathon T. Olesberg ◽  
Lingzhi Liu ◽  
Valerie Van Zee ◽  
Mark A. Arnold
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Skin Tissue ◽  
Rat Skin ◽  
Glucose Levels ◽  
Blood Glucose Levels

Net analyte signal with floating reference theory in non-invasive blood glucose sensing by near-infrared spectroscopy

2012 ◽  
Vol 10 (8) ◽  
pp. 083002-83005 ◽  
Author(s):  
Wanjie Zhang Wanjie Zhang ◽  
Rong Liu Rong Liu ◽  
Wen Zhang Wen Zhang ◽  
Jiaxiang Zheng Jiaxiang Zheng ◽  
Kexin Xu Kexin Xu
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Non Invasive ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Reference Theory

Influence of Blood Glucose Level on the Scattering Coefficient of the Skin in Near-Infrared Spectroscopy

2011 ◽  
Author(s):  
Sachiko Kessoku ◽  
Katsuhiko Maruo ◽  
Shinpei Okawa ◽  
Kazuto Masamoto ◽  
Yukio Yamada
Keyword(s):  
Optical Properties ◽  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Monitoring Methods ◽  
Absorbance Spectra

Various non-invasive glucose monitoring methods using near-infrared spectroscopy have been investigated although no method has been successful so far. Our previous study has proposed a new promising method utilizing numerically generated absorbance spectra instead of the experimentally acquired absorbance spectra. The method suggests that the correct estimation of the optical properties is very important for numerically generating the absorbance spectra. The purpose of this study is to measure the change in the optical properties of the skin with the change in the blood glucose level in vivo. By measuring the reflectances of light incident on the skin surface at two distances from the incident point, the optical properties of the skin can be estimated. The estimation is a kind of the inverse problem based on the simulation of light propagation in the skin. Phantom experiments have verified the method and in vivo experiments are to be performed.

scholarly journals Bioimpedance and NIR for non-invasive assessment of blood glucose

2019 ◽  
Vol 10 (1) ◽  
pp. 133-138 ◽  
Author(s):  
Jan-Hugo Andersen ◽  
Olav Bjerke ◽  
Fatos Blakaj ◽  
Vilde Moe Flugsrud ◽  
Fredrik Alstad Jacobsen ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Electrical Impedance ◽  
Near Infrared ◽  
Soft Drink ◽  
Electrical Impedance Spectroscopy ◽  
Electrical Measurements ◽  
High Blood Glucose ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels

Abstract Sixteen volunteers each drank 700 ml sugar-containing soft drink during two successive periods and the blood sugar was measured at 10 min intervals together with electrical impedance spectroscopy and near infrared spectroscopy (NIR). A maximum correlation of 0.46 was found for the electrical measurements but no clear separation between low and high blood glucose levels were found in the NIR measurements. The latter was attributed to the experimental design where the NIR probe was removed from the skin between each measurement.

Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep learning method

2021 ◽  
Author(s):  
Zhuyu Wang ◽  
Linhua Zhou ◽  
Tianqing Liu ◽  
Kewei Huan ◽  
Xiaoning Jia
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Blood Glucose Concentration ◽  
High Dimensional ◽  
Support Vector ◽  
Non Invasive ◽  
Blood Glucose Concentrations

Abstract Extracting micro-scale spectral features from dynamic blood glucose concentrations is extremely difficult when using non-invasive measurement methods. This work proposes a new machine-learning method based on near-infrared spectroscopy, deep belief network (DBN), and support vector machine (SVR), to improve the prediction accuracy. First, the standard oral glucose tolerance test is used to collect near-infrared spectroscopy and actual blood glucose concentration values for specific wavelengths (1200, 1300, 1350, 1450, 1600, 1610, and 1650 nm), and the blood glucose concentrations is within a clinical range of 70mg/dL~220mg/dL. Second, based on the DBN model, high-dimensional deep features of the non-invasive blood glucose spectrum are extracted. These are used to establish a support vector regression (SVR) model and to quantitatively analyze the influence of spectral sample size and corresponding feature dimensions (i.e., DBN network structure) on the prediction accuracy. Finally, based on data from six volunteers, a comparative analysis of the SVR prediction accuracy is performed both before and after using high-dimensional deep features. For volunteer 1, when the DBN-based high-dimensional deep features were used, the root mean square error (RMSE) of support vector regression (SVR) was reduced by 71.67%, the correlation coefficient (R2) and the P value of Clark grid analysis (P) were increased by 13.99% and 6.28%, respectively. Moreover, we have similar results when the proposed method was carried out on the data of other volunteers. The results show that the presented algorithm can play an important role in dynamic non-invasive blood glucose concentration prediction and can effectively improve the accuracy of the SVR model. Further, by applying the algorithm to six independent sets of data, this research also illustrates the high-precision regression and generalization capabilities of the DBN-SVR algorithm.

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