Analysis of Non-invasive Methods to Diagnose Blood Glucose Level-A Survey

2019 ◽  
Vol 12 (6) ◽  
pp. 3105
Author(s):  
J. Premalatha ◽  
P. Grace Kanmani Prince
2021 ◽  
Vol 5 (1) ◽  
pp. 14-25
Author(s):  
Nurul Fadhilah ◽  
Erfiani Erfiani ◽  
Indahwati Indahwati

The calibration method is an alternative method that can be used to analyze the relationship between invasive and non-invasive blood glucose levels. Calibration modeling generally has a large dimension and contains multicolinearities because usually in functional data the number of independent variables (p) is greater than the number of observations (p>n). Both problems can be overcome using Functional Regression (FR) and Functional Principal Component Regression (FPCR). FPCR is based on Principal Component Analysis (PCA). In FPCR, the data is transformed using a polynomial basis before data reduction. This research tried to model the equations of spectral calibration of voltage value excreted by non-invasive blood glucose level monitoring devices to predict blood glucose using FR and FPCR. This study aimed to determine the best calibration model for measuring non-invasive blood glucose levels with the FR and FPCR. The results of this research showed that the FR model had a bigger coefficient determination (R2) value and lower Root Mean Square Error (RMSE) and Root Mean Square Error Prediction (RMSEP) value than the FPCR model, which was 12.9%, 5.417, and 5.727 respectively. Overall, the calibration modeling with the FR model is the best model for estimate blood glucose level compared to the FPCR model.


2012 ◽  
Vol 59 (4) ◽  
pp. 198-204 ◽  
Author(s):  
Biljana Andjelski-Radicevic ◽  
Radica Dozic ◽  
Tatjana Todorovic ◽  
Ivan Dozic

Diabetes mellitus is metabolic syndrome characterized by disorder in metabolism of carbon hydrates, lipids and proteins. The diagnosis of diabetes is established by measuring the blood glucose level using standardized methods. Frequent monitoring of blood glucose level could be inconvenient for patients because of possible pain during blood sample taking. In the last few years biological materials with non invasive sampling, like saliva, have been analyzed. Research has confirmed that some organic and inorganic components of saliva are modified in diabetic patients (glucose, lipid and protein components, oxidative stress markers, electrolytes). Beside other markers, the analysis of glucose in saliva is an attempt to find a non-invasive and painless way for frequent monitoring of glucose concentration in diabetic patients. Collecting saliva is simple and economical, it neither requires expensive equipment nor specially trained staff. Saliva can be taken many times and in unlimited quantity. In regards to the data about the possibilities for using saliva as biological sample in monitoring diabetes mellitus, which could be alternative to blood serum or plasma, the conclusion is that saliva becomes more important in this context.


2020 ◽  
Vol 32 (06) ◽  
pp. 2050043
Author(s):  
Keshava N. Acharya ◽  
M. G. Yashwanth Gowda ◽  
M. Vijay ◽  
S. Deepthi ◽  
S. Malathi ◽  
...  

Blood glucose monitoring systems (BGMSs) play a crucial role in health care applications. Invasive measurements are more accurate while non-invasive BGMS encourage self monitoring and reduce the cost of health care. Though multiple sensor data acquisition and suitable processing improve accuracy, self-monitoring becomes difficult in such non-invasive systems due to multiple signal acquisition. This paper investigates a non-invasive BGMS prototype that renders accurate measurements by statistically processing a single sensor data. The developed prototype is based on near-infrared (NIR) spectroscopy, which provides an electronic voltage that gets mapped to corresponding blood glucose level. This mapping is proposed using two different statistical regression approaches, parametric Bayesian Regression (BR) approach and the non-parametric Gaussian Process Regression (GPR) approach. Dataset is acquired from 33 subjects who visited Ramaiah Medical College Hospital, India. On each subject, voltage from the BGMS prototype and corresponding invasively obtained blood glucose level have been recorded. The BR and GPR approaches are trained with 75% of the data while the remaining 25% is used for testing. Test results show that BR approach renders root mean square error (RMSE) of 3.7[Formula: see text]mg/dL, while the mean absolute percentage error (MAPE) is around 2.5. The GPR with different radial basis function kernels revealed that a multiquadric kernel provides a lowest RMSE of 3.28[Formula: see text]mg/dL and lowest MAPE of 2.2, thus outperforming the parametric BR approach. Investigations also show that for a training data of less than 15 entries, BR renders better accuracy than the GPR approach.


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