The discussion about the complexity of samples and the prediction accuracy of glucose concentration in non-invasive blood glucose sensing

2005 ◽  
Author(s):  
Xiaoyu Gu ◽  
Rong Liu ◽  
Wenliang Chen ◽  
Kexin Xu
Keyword(s):  
Blood Glucose ◽  
Glucose Concentration ◽  
Prediction Accuracy ◽  
Glucose Sensing ◽  
Non Invasive

scholarly journals A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6820
Author(s):  
Bushra Alsunaidi ◽  
Murad Althobaiti ◽  
Mahbubunnabi Tamal ◽  
Waleed Albaker ◽  
Ibraheem Al-Naib
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Daily Basis ◽  
Glucose Sensing ◽  
Blood Glucose Monitoring ◽  
Optical Systems ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

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 cholesterol on non-invasive blood glucose sensing studied with NIR spectroscopy

2011 ◽  
Author(s):  
Jingying Jiang ◽  
Lingling Zhang ◽  
Qiliang Gong ◽  
Kexin Xu
Keyword(s):  
Blood Glucose ◽  
Nir Spectroscopy ◽  
Glucose Sensing ◽  
Non Invasive

scholarly journals Non-Invasive Blood Glucose Monitoring Technology: A Review

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6925
Author(s):  
Liu Tang ◽  
Shwu Jen Chang ◽  
Ching-Jung Chen ◽  
Jen-Tsai Liu
Keyword(s):  
Blood Glucose ◽  
Rapid Development ◽  
Glucose Monitoring ◽  
Glucose Sensing ◽  
Blood Glucose Monitoring ◽  
Research Progress ◽  
Monitoring Technology ◽  
Vast Number ◽  
Non Invasive ◽  
Number Of Patients

In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

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.

Sign in / Sign up

Export Citation Format

Share Document