Non-invasive Glucose Research Articles

Optical methods of noninvasive glucose measurement

In recent decades, the need to develop non-invasive methods for measuring blood glucose levels to facilitate continuous monitoring of this indicator has increased significantly. This is due to a large increase in the incidence of diabetes worldwide. However, there are many problems associated with non-invasive glucose monitoring that contribute to inaccurate results. This article discusses and classifies non-invasive methods for measuring glucose based on its properties over the past 20 years. The main focus is on the relevant physiological aspects, existing problems, and recent advances that have made it possible to obtain acceptable clinical accuracy.

Optical blood glucose non-invasive detection and its research progress.

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Non-invasive glucose detection with smart textiles

The increasing prevalence of diabetes worldwide has heightened the need for innovative and patient-friendly glucose monitoring technologies. Wearable devices that integrate non-invasive glucose sensors into everyday clothing can further enhance user comfort and seamlessly fit into daily routines, making health management more intuitive and less intrusive.

Application of Biosensors in Non-invasive Blood Glucose Monitoring

Blood glucose monitoring is essential for managing conditions like diabetes and liver dysfunction, with a growing need among various population groups, including the elderly, pregnant, and individuals engaging in specific dietary and exercise regimes. The technology for glucose monitoring is divided into invasive and non-invasive methods. Invasive techniques, while accurate, involve bodily fluid extraction, posing risks of infection and inconvenience for frequent monitoring. Conversely, non-invasive methods, which include biosensors that analyze internal signals, offer a safer and more user-friendly alternative, providing instant feedback on glucose levels. Recent advancements in biosensor technology have focused on enhancing accuracy, biocompatibility, and ease of use, with significant progress in areas like optical and electrochemical sensors. These developments aim to offer more reliable, convenient, and comprehensive glucose monitoring solutions, leveraging innovations in sensor technology and data analysis to cater to the diverse needs of individuals requiring blood glucose tracking.

PPG based Noninvasive Blood Glucose Monitoring using Multi-view Attention and Cascaded BiLSTM Hierarchical Feature Fusion Approach.

Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

Near-infrared Imaging and Convolutional Neural Network for Non-Invasive Blood Glucose Monitoring

Near-infrared Imaging and Convolutional Neural Network for Non-Invasive Blood Glucose Monitoring

Non-invasive blood glucose monitoring technology in diabetes management: review.

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.

Design of Non-Invasive Blood Glucose Detection Microwave Sensor Based on CSRR

Design of Non-Invasive Blood Glucose Detection Microwave Sensor Based on CSRR

Green synthesized gold nanoparticles and CuO-based nonenzymatic sensor for saliva glucose monitoring.

Glucose, essential for brain and muscle functions, requires careful monitoring in diabetes and other chronic disease management. While blood glucose monitoring provides precise information about these diseases, it remains an invasive method. Saliva glucose monitoring could offer an alternative approach, but the glucose concentration in saliva is very low. In this work, we report a simple, low-cost, highly sensitive nonenzymatic electrochemical glucose sensor. We developed this sensor using green synthesized gold nanoparticles (AuNPs) and wet chemical synthesized copper oxide (CuO) nanoparticles on a screen-printed carbon electrode (Au/CuO/SPCE). The sensor's high sensitivity results from dual amplification strategies using AuNPs and CuO nanomaterials, each demonstrating catalytic activity towards glucose. This shows promising potential for saliva glucose monitoring. The AuNPs were synthesized using an Au precursor and orange peel extract (OPE), yielding stable colloidal AuNPs with a mean diameter of about 37 nm, thus eliminating the need for additional capping agents. Under optimal conditions, amperometric tests revealed that the sensor responded linearly to glucose concentrations ranging from 2 μM to 397 μM with a sensitivity of 236.70 μA mM-1 cm-2. Furthermore, the sensor demonstrated excellent reproducibility, stability and high selectivity for glucose in the presence of different biomolecules. We validated the sensor's efficacy by measuring glucose in human saliva, showing its potential for noninvasive glucose monitoring. This research advances the development of point-of-care devices, positioning the sensor as a promising tool for noninvasive glucose monitoring and improved diabetes management.

Surface engineered metal-organic framework-based electrochemical biosensors for enzyme-mimic ultrasensitive detection of glucose: recent advancements and future perspectives.

Metal-Organic Frameworks (MOFs) have garnered significant attention in the development of electrochemical glucose sensors due to their unique and advantageous properties. The highly tunable pore channels of MOFs facilitate optimal diffusion of glucose molecules, while their large specific surface area provides abundant active sites for electrochemical reactions. Furthermore, the well-dispersed metallic active sites within MOFs enhance electrocatalytic activity, thereby improving the sensitivity and selectivity of glucose detection. These features make MOF-based nanoarchitectures promising candidates for the development of efficient and sensitive glucose sensors, which are crucial for diabetes management and monitoring. The integration of enzymatic biosensors with nanotechnology continues to drive advancements in glucose monitoring, offering the potential for more accurate, convenient, and user-friendly tools for diabetes management. Current research explores non-invasive glucose monitoring methods, such as using sweat, saliva, or interstitial fluid instead of blood, aiming to reduce the discomfort and inconvenience associated with frequent blood sampling. A review of the advancements and applications of MOF-based enzyme-mimic electrochemical sensors for glucose monitoring can provide valuable insights for young researchers, inspiring future research in biomedical device fabrication. Such reviews not only offer a comprehensive understanding of the current state of the art but also highlight existing challenges and future opportunities in the field of enzyme-less glucose sensing, particularly in the surface modification techniques of highly porous MOFs. This fosters innovation and new research directions. By understanding the advantages, challenges, and opportunities, researchers can contribute to the development of more effective and innovative enzyme-mimic glucose sensing transducers, which are essential for advancing biomedical devices.

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 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.

GlucoBreath: An IoT, ML, and Breath-Based Non-Invasive Glucose Meter

GlucoBreath: An IoT, ML, and Breath-Based Non-Invasive Glucose Meter

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

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

Using an LED light source coupled with spectral image analysis for non-invasive glucose detection

Using an LED light source coupled with spectral image analysis for non-invasive glucose detection

Towards Noninvasive Glucose Monitoring Based on Bioimpedance Grid Sampling Topology

Towards Noninvasive Glucose Monitoring Based on Bioimpedance Grid Sampling Topology

Analysis of Non-Invasive Devices and Techniques Involved in Continuous Monitoring of Interstitial Glucose of Patients with Type 1 Diabetes

Abstract: There are 422 million diabetes patients worldwide according to 2023 census of WHO, and around 100 million cases in India. 8.4 million type 1 diabetes cases were recorded as of 2021. By 2040, it is projected that the number of people living with Type 1 Diabetes will reach 13.5–17.4 million. The surge in diabetes cases has created the critical need for regular glucose monitoring to effectively manage this prevalent health issue. Currently, glucose levels are predominantly measured invasively by pricking the finger for a blood sample and involves invasive methods, such as fingertip pricks, which can be uncomfortable and inconvenient, particularly for paediatric patients. This method utilizes blood glucose monitoring devices, which measure sugar levels in a small blood sample placed on a disposable test strip and have a certain degree of inaccuracy. Patients do not test as often as they should because of the pain and hassle of finger pricks and many do not achieve optimal glycaemic control. However, the shift toward non-invasive continuous glucose monitoring is becoming increasingly imperative and a promising alternative. With current biotechnological advances, many techniques offer a non-invasive, continuous means of monitoring glucose levels and have garnered significant attention in recent years. The objective is to consolidate and summarize recent advancements in non-invasive continuous glucose monitoring, emphasizing integration into wearable platforms for seamless and real-time monitoring. Furthermore, paediatric patients with Type 1 Diabetes face elevated risks of severe hypoglycaemic and hyperglycaemic events due to challenges in glycaemic control. Frequently, monitoring blood glucose is crucial to mitigate these risks and achieve optimal glycaemic management. The objective of ongoing research is to validate AI-based algorithms utilizing ECG signals collected through non-invasive devices to detect glycaemic events. These advancements, once commercially available, have the potential to revolutionize glucose monitoring, offering a convenient, efficient, and less intrusive approach, particularly benefiting paediatric patients and improving overall healthcare management. Despite substantial research and development, challenges persist, including standardizing sweat collection methods, addressing sample degradation, accounting for variations in sweat composition among individuals, optimizing diverse detection methods, improving glucose detection sensitivity, and ensuring overall commercial viability. Overcoming these challenges is paramount to bridge the gap between research and market availability, paving the way for a technological revolution in glucose monitoring. In this study, we compared potential non-invasive glucose monitoring devices and techniques to evaluate the most accurate, pain-free, fast, and cost-friendly device. Upon examination and survey, we have theorized the drawbacks persisting in the existing devices and hypothesized techniques. Looking forward this study has a broader application in not only understanding the limitations and creating more accurate devices but also in continuous glucose monitoring for diabetic patient care.

Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.

Measurement Analysis of Non-Invasive Blood Glucose On Sensor Coplanar Waveguide Loaded Square Ring Resonator with Interdigital Coupling Capacitor

This study presents the experimental results of a system with a sensor structure detecting human blood glucose levels. A microwave-based sensor is used for non-invasive blood glucose monitoring. The sensor design uses an asymmetrically loaded CPW structure as a square ring resonator with an interdigital coupling capacitor on the ground side. Simulated with a load of artificial finger tissue made from gelatin, modeled in four layers. The first layer is the skin is the outermost tissue, the next layer is fat, blood and bone. Each layer of tissue has a certain thickness size; skin (0.3mm), fat (0.2mm), blood (1.5mm), and bone (4mm). The measurement simulation is used, HFSS as modeling simulation and VNA as a measurement of the physical representation of the design results with parametric optimization methods. To verify the correlation and the expected sensitivity, media with different dielectrics were mounted on the surface of the sensor resonator with blood glucose levels of 1mg/dl, 72mg/dl, 126mg/dl, 162mg/dl and 216mg/dl. Reflection factor S11 was observed based on dielectric constant blood glucose levels (dB) fluctuations. Analysis of the data on the graph between the independent variables, namely blood glucose concentration and the dependent variable levels of S11 has an “R” correlation value of 0.97. The sensitivity level of the sensor on the S11 reflection factor with HFSS simulation averages 73.36mdB/mgdl-1 and VNA reaches 82.39mdB/mgdl-1. The results are interesting for developing a more optimal glucose sensor system.

Measurement Analysis of Non-Invasive Blood Glucose On Sensor Coplanar Waveguide Loaded Square Ring Resonator with Interdigital Coupling Capacitor

This study presents the experimental results of a system with a sensor structure detecting human blood glucose levels. A microwave-based sensor is used for non-invasive blood glucose monitoring. The sensor design uses an asymmetrically loaded CPW structure as a square ring resonator with an interdigital coupling capacitor on the ground side. Simulated with a load of artificial finger tissue made from gelatin, modeled in four layers. The first layer is the skin is the outermost tissue, the next layer is fat, blood and bone. Each layer of tissue has a certain thickness size; skin (0.3mm), fat (0.2mm), blood (1.5mm), and bone (4mm). The measurement simulation is used, HFSS as modeling simulation and VNA as a measurement of the physical representation of the design results with parametric optimization methods. To verify the correlation and the expected sensitivity, media with different dielectrics were mounted on the surface of the sensor resonator with blood glucose levels of 1mg/dl, 72mg/dl, 126mg/dl, 162mg/dl and 216mg/dl. Reflection factor S11 was observed based on dielectric constant blood glucose levels (dB) fluctuations. Analysis of the data on the graph between the independent variables, namely blood glucose concentration and the dependent variable levels of S11 has an “R” correlation value of 0.97. The sensitivity level of the sensor on the S11 reflection factor with HFSS simulation averages 73.36mdB/mgdl-1 and VNA reaches 82.39mdB/mgdl-1. The results are interesting for developing a more optimal glucose sensor system.