Non-invasive Blood Glucose Monitoring System Research Articles

Internet of Things enabled open source assisted real-time blood glucose monitoring framework

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

Design and Verification of Noninvasive Wearable Continuous Blood Glucose Monitoring System for Smartwatches

Design and Verification of Noninvasive Wearable Continuous Blood Glucose Monitoring System for Smartwatches

Everything You Wanted to Know About Continuous Glucose Monitoring

Diabetes is a chronicle disease where the body of a human is irregular to dissolve the blood glucose properly. The diabetes is due to lack of insulin in human body. The continuous monitoring of blood glucose is main important aspect for health care. Most of the successful glucose monitoring devices is based on methodology of pricking of blood. However, such kind of approach may not be advisable for frequent measurement. The paper presents the extensive review of glucose measurement techniques. The paper covers various non-invasive glucose methods and its control with smart healthcare technology. To fulfill the imperatives for non-invasive blood glucose monitoring system, there is a need to configure an accurate measurement device. Noninvasive glucose-level monitoring device for clinical test overcomes the problem of frequent pricking for blood samples. There is requirement to develop the Internet-Medical-Things (IoMT) integrated Healthcare Cyber-Physical System (H-CPS) based Smart Healthcare framework for glucose measurement with purpose of continuous health monitoring. The paper also covers selective consumer products along with selected state of art glucose measurement approaches. The paper has also listed several challenges and open problems for glucose measurement.

PARAMETRIC AND NON-PARAMETRIC REGRESSION APPROACHES FOR NON-INVASIVE BLOOD GLUCOSE MONITORING

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.

A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning

Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.

Noninvasive blood glucose monitoring system based on near-infrared method

Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).

Non-invasive Glucose Monitoring at mm-Wave Frequencies

In this paper, we review our recent experimental studies todevelop a non-invasive blood glucose monitoring system at mm-wave frequencies. A robust 60 GHz radar system is used todifferentiate between hemoglobin samples of disparate glucoseconcentrations through detecting minute changes in theirdielectric properties. A new-developed dielectric assessmentsystem (DAK) is used to characterise the electromagneticproperties of the tested samples and identify the amount ofchange in these parameters at different concentrations of interest.

Modelling, verification, and calibration of a photoacoustics based continuous non-invasive blood glucose monitoring system.

This paper examines the use of photoacoustic spectroscopy (PAS) at an excitation wavelength of 905 nm for making continuous non-invasive blood glucose measurements. The theoretical background of the measurement technique is verified through simulation. An apparatus is fabricated for performing photoacoustic measurements in vitro on glucose solutions and in vivo on human subjects. The amplitude of the photoacoustic signals measured from glucose solutions is observed to increase with the solution concentration, while photoacoustic amplitude obtained from in vivo measurements follows the blood glucose concentration of the subjects, indicating a direct proportionality between the two quantities. A linear calibration method is applied separately on measurements obtained from each individual in order to estimate the blood glucose concentration. The estimated glucose values are compared to reference glucose concentrations measured using a standard glucose meter. A plot of 196 measurement pairs taken over 30 normal subjects on a Clarke error grid gives a point distribution of 82.65% and 17.35% over zones A and B of the grid with a mean absolute relative deviation (MARD) of 11.78% and a mean absolute difference (MAD) of 15.27 mg/dl (0.85 mmol/l). The results obtained are better than or comparable to those obtained using photoacoustic spectroscopy based methods or other non-invasive measurement techniques available. The accuracy levels obtained are also comparable to commercially available continuous glucose monitoring systems.

Noninvasive Blood Glucose Monitoring System Based on Distributed Multi-Sensors Information Fusion of Multi-Wavelength NIR

In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root- Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coefficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri- buted multi-sensors, shows better prediction accuracy.

Clinical Application of Non-invasive Blood Glucose Monitoring System by Infrared Spectroscopy

Recently, the number of diabetics has steadily increased, and they have to measure their blood glucose level again and again each day. We invented a non-invasive system for measuring blood glucose level based on infrared spectroscopy of attenuated total reflection (ATR). And, using a PLS (partial least squares) calibration model we could measure the blood glucose level of diabetics. The purpose is to examine the accuracy of this monitoring system in clinical trials. And the difference in reflection times of infrared in an ATR prism was examined.

Development of Non-invasive Blood Glucose Monitoring System based on Infrared Spectroscopy

Over the past few decades, an increasing number of studies have been done on the measurement of blood glucose. We developed a noninvasive system to measure blood glucose based on infrared spectroscopy (IR). With this system, we were able to measure the spectrum of the human finger by using Fourier transform infrared spectroscopy - attenuated total reflectance (FTIR-ATR). Furthermore, by using an individual calibration model based on partial least squares regression, we were able to measure human blood glucose. We describe the measurement results and assess the validity of the new system in this paper.

Noninvasive Near-Infrared Blood Glucose Monitoring Using a Calibration Model Built by a Numerical Simulation Method: Trial Application to Patients in an Intensive Care Unit

We have applied a new methodology for noninvasive continuous blood glucose monitoring, proposed in our previous paper, to patients in ICU (intensive care unit), where strict controls of blood glucose levels are required. The new methodology can build calibration models essentially from numerical simulation, while the conventional methodology requires pre-experiments such as sugar tolerance tests, which are impossible to perform on ICU patients in most cases. The in vivo experiments in this study consisted of two stages, the first stage conducted on healthy subjects as preliminary experiments, and the second stage on ICU patients. The prediction performance of the first stage was obtained as a correlation coefficient (r) of 0.71 and standard error of prediction (SEP) of 28.7 mg/dL. Of the 323 total data, 71.5% were in the A zone, 28.5% were in the B zone, and none were in the C, D, and E zones for the Clarke error-grid analysis. The prediction performance of the second stage was obtained as an r of 0.97 and SEP of 27.2 mg/dL. Of the 304 total data, 80.3% were in the A zone, 19.7% were in the B zone, and none were in the C, D, and E zones. These prediction results suggest that the new methodology has the potential to realize a noninvasive blood glucose monitoring system using near-infrared spectroscopy (NIRS) in ICUs. Although the total performance of the present monitoring system has not yet reached a satisfactory level as a stand-alone system, it can be developed as a complementary system to the conventional one used in ICUs for routine blood glucose management, which checks the blood glucose levels of patients every few hours.

Glucose content in human skin: relationship with blood glucose levels.

In order to ascertain the dynamic relationship between the extracellular glucose in upper skin layers and blood glucose, skin suction blisters were raised in six Type 1 diabetic patients during a three-step glucose clamp. Blister glucose closely paralleled venous glucose (mean of r = 0.998). However, in three patients blister glucose was constantly lower than plasma glucose and this appeared to be related to their slower formation of skin blisters. A substantial difference in skin blister suction time was noted among patients and it was found that suction time was linearly correlated to glycosylated haemoglobin (HbA1C) (n = 6, r = 0.865, p = 0.026). It is concluded that a non-invasive blood glucose monitoring system could be successfully based on measurement of alterations in skin glucose contents.