Non-invasive Glucose Measurement Research Articles

Advancing Glucose Monitoring: A Near-Infrared Device for Continuous Invasive Assessment

Diabetes is a chronic disease that affects millions of people worldwide, with the number of newly diagnosed cases continuing to rise at an alarming rate. Patients diagnosed with diabetes require routine monitoring of their blood glucose levels, which is commonly done through invasive and minimally invasive techniques. However, these methods are often painful, prone to infection, and costly in the long run. Therefore, it is necessary to develop non-invasive, pain-free, affordable glucose monitoring techniques. Near-infrared spectroscopy has emerged as a promising technique among the non-invasive techniques for glucose monitoring, with several studies reporting its efficacy in detecting glucose concentration. This study designed and developed a near-infrared system based on an 1150nm wavelength for continuous non-invasive glucose monitoring. The system gave the output voltages that vary with increased glucose concentrations. It was ascertained from the Clark error grid analysis that the glucose concentration falls within region A and region B. This implies that the device is suitable for measuring glucose concentration non-invasively. As per Bland Altman's analysis, the majority of the data points were within both limits of agreement except one data point that was outside the upper limit of agreement, indicating 93% degree of accuracy, and the residual plot indicates that the regression model for the system is appropriate for predicting non-invasive glucose measurements.

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.

Non-invasive glucose measurements : where do we stand?

The technologies used to measure blood glucose have significantly evolved the past few years, especially with the introduction of continuous interstitial glucose measurements, simplifying the management of the disease. More recently, there has been a lot of interest regarding some potential revolutionary methods, such as smartwatches, and glucose measurements in sweat, saliva, and even tears. In this article, we review the different technologies that are under development, and notice that although promising, they rest imprecise. False measurements can have fatal consequences for our patients. Nevertheless, these innovations are promising and have the potential to change the daily life of people with diabetes in the future.

Functional Continuum Regression Approach to Wavelet Transformation Data in a Non-Invasive Glucose Measurement Calibration Model

Functional Continuum Regression Approach to Wavelet Transformation Data in a Non-Invasive Glucose Measurement Calibration Model

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

A photonic crystal fibre-based sensor for glucose concentration detection

In this study, we present a glucose sensor that utilises photonic crystal fibre. The proposed sensor was analysed and demonstrated numerically. A unique structure was infiltrated into the first ring of holes with glucose, serving as the test analyte. We achieved an exceptionally high average spectral sensitivity. Three different wavelengths, 1.1, 1.3 and 1.55 µm, were selected for this investigation. Notably, while the reported sensitivity was already high when the remaining gaps within the structure were filled with air, it was significantly higher when the holes were infiltrated with water at a wavelength of 1.55 µm, calculated to be 0.11493 ps/(nm·km)/(g/l). We anticipate that this sensor will prove to be a valuable tool for glucose sensing and could contribute to further advancements in non-invasive glucose measurement.

Learned prediction of cholesterol and glucose using ARIMA and LSTM models – A comparison

Painless remedies and diagnosis have become the primary objective in medical sciences as diseases shoot up. This research addresses the pressing need for cost-effective, non-invasive, and real-time monitoring solutions for glucose and cholesterol levels to reduce mortality rates associated with diabetes and cardiovascular diseases. Diabetes and cardiovascular diseases are significant global health challenges, affecting millions of individuals, particularly in low to middle-income countries. The inconvenience and discomfort of traditional blood tests have often resulted in delayed testing and monitoring, making it imperative to develop alternative methods for early disease detection and continuous monitoring. The primary objective of this research is to create a non-invasive glucose and cholesterol monitoring and forecasting system that leverages Artificial Intelligence and Cloud computing. The specific focus is comparing the prediction efficiency of two models: the Auto-Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. The research methodology involves the development of a hardware prototype for the non-invasive measurement of glucose and cholesterol, employing Near Infra-Red (NIR) sensors. Data on glucose and cholesterol levels are collected and stored from patients over a period of one month. The collected data is used to train both the ARIMA and LSTM models. The models are evaluated using Root Mean Square Error (RMSE), and their prediction accuracy is compared. The study findings reveal that the ARIMA model surpasses the LSTM model in predicting glucose and cholesterol levels. This is supported by a lower RMSE of the ARIMA model (about 71.7 % less for Glucose and 50.3 % less for Cholesterol) compared to the LSTM model, indicating higher prediction accuracy.Additionally, the hardware prototype facilitates non-invasive measurement and forecasting, offering a promising solution for improved early diagnosis and monitoring. The research highlights the potential of ARIMA and LSTM models in healthcare and diagnostic applications. The superior performance of the ARIMA model in predicting glucose and cholesterol levels positions it as a valuable tool for real-time applications. This study provides a unified solution for non-invasive measurement and forecasting, aiming to enhance early diagnosis and monitoring of diabetes and cardiovascular risk.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians.

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.

Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement

Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. Therefore, we aim to find the impact of other factors on the proper calculation of HRV. In this paper, we research the relation between HRV and the age and gender of the patient to adjust the threshold correspondingly to the noninvasive glucose estimator that we are developing and improve its performance. While most of the literature research so far addresses healthy patients and only short- or long-term HRV, we apply a more holistic approach by including both healthy patients and patients with arrhythmia and different lengths of HRV measurements (short, middle, and long). The methods necessary to determine the correlation are (i) point biserial correlation, (ii) Pearson correlation, and (iii) Spearman rank correlation. We developed a mathematical model of a linear or monotonic dependence function and a machine learning and deep learning model, building a classification detector and level estimator. We used electrocardiogram (ECG) data from 4 different datasets consisting of 284 subjects. Age and gender influence HRV with a moderate correlation value of 0.58. This work elucidates the intricate interplay between individual input and output parameters compared with previous efforts, where correlations were found between HRV and blood glucose levels using deep learning techniques. It can successfully detect the influence of each input.

The effect of temperature on permittivity measurements of aqueous solutions of glucose for the development of non-invasive glucose sensors based on electromagnetic waves

This article presents for the first time an empirical study that shows the importance of considering temperature when analyzing the permittivity (dielectric constant) of aqueous glucose solutions of various concentrations. The permittivity is a parameter that is investigated by researchers as a biomarker for non-invasive measurement of glucose without drawing blood. The development of this technology will allow personalized healthcare diagnostics to monitor and prevent diabetes. Since human glucose levels in the blood vary in the range of a few milligrams per decilitre, estimating such small variations of glucose will require a highly accurate and repeatable sensing technology. Electromagnetic (EM) waves, specifically in the microwave and terahertz frequency ranges, have shown promise in detecting changes in the electrical properties of blood plasma as they relate to glucose concentration. However, it's important to note that while this technology shows promise, it is still in the research and development phase. It is shown here that the body temperature can affect the accuracy of the blood glucose measurements. Experiments were conducted with different glucose concentration solutions under various temperatures and the complex permittivity of the glucose was studied across a wide frequency range from 400 MHz to 11 GHz. The rise in thermal energy normally causes dipolar liquids like water to vibrate and rotate disrupting the alignment of the dipoles in response to an electric field thereby reducing its permittivity. Empirical results however show that for aqueous solution of glucose the permittivity increases with rise in temperature from 16 °C to 37 °C. This is attributed to the polar nature of the water and glucose molecules that becomes more pronounced with increased thermal energy. Based on the experimental results an accurate analytical expression is derived that considers the temperature of the aqueous glucose solution. The accuracy of the analytical expression is shown experimentally to be above 99%. The findings from the study should enable the design of accurate non-invasive glucose monitoring devices based on electromagnetic sensing techniques.

Overview of modern sensors for continuous glucose monitoring

The incidence of diabetes is increasing in all age groups. The use of technological devices for the treatment of diabetes, such as continuous glucose monitoring (CGM), is expanding and is associated with improved control of blood glucose levels in order to prevent complications of this disease. Reducing glycemic variability and maintaining optimal glycemic control is critical to the management of patients with type 1 diabetes. The usefulness of glycemic monitoring devices has also been shown for patients with type 2 diabetes. CGM technology is constantly being improved in terms of analytical performance, biocompatibility, wear duration, safety and clinical performance. However, commonly used minimally invasive CGMs do not measure blood glucose directly, but instead measure the glucose concentration in the interstitial fluid (IF), so changes in IF glucose occur with a delay of 5 to 15 minutes compared to blood glucose. In addition, the lifetime of minimally invasive CGM sensors is relatively short, up to 14 days. Therefore, the introduction into clinical practice of devices for non-invasive glucose measurement in people with diabetes, which overcome the above-mentioned limitations of minimally invasive CGM, will expand the possibilities of glucose monitoring among patients with diabetes. The purpose of this review was to present the technologies of CGM system sensors approved for medical use in Russia and other countries.

Non-invasive glucose measurement using 950 nm reflective short wave NIR technique

Non-invasive glucose measurement using 950 nm reflective short wave NIR technique

Patent analysis of digital sensors for continuous glucose monitoring.

The high need for optimal diabetes management among an ever-increasing number of patients dictates the development and implementation of new digital sensors for continuous glucose monitoring. The purpose of this work is to systematize the global patenting trends of digital sensors for continuous glucose monitoring and analyze their effectiveness in controlling the treatment of diabetes patients of different ages and risk groups. The Lens database was used to build the patent landscape of sensors for continuous glucose monitoring. Retrospective analysis showed that the patenting of sensors for continuous glucose monitoring had positive trend over the analyzed period (2000-2022). Leading development companies are Dexcom Inc., Abbott Diabetes Care Inc., Medtronic Minimed Inc., Roche Diabetes Care Inc., Roche Diagnostics Operations Inc., Roche Diabetes Care Gmbh, and Ascensia Diabetes Care Holdings Ag, among others. Since 2006, a new approach has emerged where digital sensors are used for continuous glucose monitoring, and smartphones act as receivers for the data. Additionally, telemedicine communication is employed to facilitate this process. This opens up new opportunities for assessing the glycemic profile (glycemic curve information, quantitative assessment of the duration and amplitude of glucose fluctuations, and so on), which may contribute to improved diabetes management. A number of digital sensors for minimally invasive glucose monitoring are patented, have received FDA approval, and have been on the market for over 10 years. Their effectiveness in the clinic has been proven, and advantages and disadvantages have been clarified. Digital sensors offer a non-invasive option for monitoring blood glucose levels, providing an alternative to traditional invasive methods. This is particularly useful for patients with diabetes who require frequent monitoring, including before and after meals, during and after exercise, and in other scenarios where glucose levels can fluctuate. However, non-invasive glucose measurements can also benefit patients without diabetes, such as those following a dietary treatment plan, pregnant women, and individuals during fasting periods like Ramadan. The availability of non-invasive monitoring is especially valuable for patients in high-risk groups and across different age ranges. New world trends have been identified in the patenting of digital sensors for non-invasive glucose monitoring in interstitial skin fluid, saliva, sweat, tear fluid, and exhaled air. A number of non-invasive devices have received the CE mark approval, which confirms that the items meet European health, safety, and environmental protection standards (TensorTip Combo-Glucometer, Cnoga Medical Ltd.; SugarBEAT, Nemaura Medical; GlucoTrack, GlucoTrack Inc.), but are not FDA-approved yet. The above-mentioned sensors have characteristics that make them popular in the treatment of diabetes: they do not require implantation, do not cause an organism reaction to a foreign body, and are convenient to use. In the EU, in order to increase clinical safety and the level of transparency about medical devices, manufacturers must obtain certificates in accordance with Regulation (EU) 2017/745, taking into account the transition period. The development of systems, which include digital sensors for continuous glucose monitoring, mobile applications, and web platforms for professional analysis of glycemic control and implementation of unified glycemic assessment principles in mobile healthcare, represent promising approaches for controlling glycaemia in patients.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Spectral analysis of multiple scattering factors of turbid media for glucose measurement using near-infrared spectroscopy.

Near-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index. Our aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement. We present a theoretical analysis of the spectra of 1000 to 1700nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution. We found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700nm. Our findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.

Integrated Device Based on a Sudomotor Nanomaterial for Sweat Detection

The compositions of sweat and blood are related. Therefore, sweat is an ideal noninvasive test body fluid that could replace blood for linear detection of many biomarkers, especially blood glucose. However, access to sweat samples remains limited to physical exercise, thermal stimulation, or electrical stimulation. Despite intensive research, a continuous, innocuous, and stable method for sweat stimulation and detection has not yet been developed. In this study, a nanomaterial for a sweat-stimulating gel based on the transdermal drug delivery system is presented, which transports acetylcholine chloride into the receptors of sweat glands to achieve the function of biological stimulation of skin sweating. The nanomaterial was applied to a suitable integrated sweat glucose detection device for noninvasive blood glucose monitoring. The total amount of evaporated sweat enabled by the nanomaterial is up to 35 μL·cm–2 for 24 h, and the device detects up to 17.65 μM glucose under optimal conditions, showing stable performance regardless of the user’s activity level. In addition, the in vivo test was performed and compared with several studies and products, which showed excellent detection performance and osmotic relationship. The nanomaterial and associated integrated device represent a significant advance in continuous passive sweat stimulation and noninvasive sweat glucose measurement for point-of-care applications.

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy.

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Designing Highly Sensitive Microwave Antenna Sensor with Novel Model for Noninvasive Glucose Measurements

Designing Highly Sensitive Microwave Antenna Sensor with Novel Model for Noninvasive Glucose Measurements

Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare.

The patients of diabetes require to observe and control their glycemic profile through continuous glucose level monitoring. The blood glucose measurement is possible through invasive, minimally invasive and non-invasive methods. Invasive method is traditional method for instant glucose measurement where glucose is measured by taking blood samples from the body. However, the repeated finger pricking increases the risk of blood-related infections and trauma. Hence, the development of non-invasive real time device is essential for smart healthcare to manage glucose-insulin balance. The paper presents machine learning models for non-invasive glucose measurement. So, various machine learning algorithms including Logistic Regression, KNN, Gaussian Naive Bayes, Linear Regression, Multi-polynomial Regression, Neural Network, XGBoost, Decision Tree, Random Forest and Support Vector Machine are applied on two dataset which are PIDD (UCI repository) and iGLU dataset (iGLU device). The comparative analysis is carried out where accuracy, training time, recall, precision, f-1 score and AUC curve is measured for classification algorithms. For regression algorithms, measures like accuracy, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used for comparison purpose. Random forest with 84% accuracy and 68% recall, 76% precision and 72% f1-score for PIDD and Decision tree with 70% accuracy, 8% mean absolute error (MAE) and 8.5% root mean square error (RMSE) for iGLU dataset gives best results. Clark grid analysis has also been done where all the values fall under zone A which gives 100% accuracy and the device is useful for medication purpose. The proposed work has been also compared with similar methods and the proposed work has excellent results in terms of MAD, mARD, RMSE and AvgE. The device would be ideal as non-invasive solution for continuous glucose monitoring.Graphical abstract