Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

Abstract

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

“…Furthermore, the early stopping method avoided over-training of the model. In comparison with [12,13,[15][16][17]21,[25][26][27], which served as the standard for predicting blood glucose, the proposed blood glucose estimation system had an MSE, RMSE, MAE, MARD, and R 2 of 40.736, 6.3824, 5.0896, 4.4321, and 0.997, respectively, all of which fell within region A of the Clarke EGA.…”

The Patch-type Multi-lead Electrocardio Multiparameter Monitoring Diagnostic Instruments and Their New-type Wireless Remote Connected Ecosystem

“…This headmounted monitor collects the photoelectric volume pulse wave signal, temperature, humidity, and thermal radiation temperature of the user's forehead or local tissue of human body, temperature and humidity of the surrounding environment for the user's forehead or the local tissue through using the re ected photoelectric volume pulse wave sensor measuring unit, temperature and humidity sensor measuring unit and thermal radiation temperature sensor measuring unit. These datas are used to estimate blood sugar levels by calculating the metabolic rate, oxygen saturation, pulse rate and blood ow velocity of the user's forehead or local tissue 46,47,48 .…”

“…We quote a non-invasive blood glucose concentration detection method 47 based on conservation of energy metabolism to test and verify the effectiveness and accuracy of the head-mounted monitor in detecting user's blood glucose. According to the method in References 47 , 40 volunteers were recruited for the experiment.…”

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

“…To measure blood glucose concentration, a mathematical model based on energy metabolism conservation was proposed [ 151 ]. To this end, a multisensor integrated diagnosis probe put on the wrist was developed.…”

“…Back-propagation neural network (BPNN) and multivariate polynomial regression [151] 540 patients with T2DM…”