Non-invasive Blood Glucose Research Articles

Highly sensitive blood glucose monitoring sensor with adjustable resonant frequency based on MP-CSRR

In this paper, a multi parallel CSRR (MP-CSRR) sensor for non-invasive blood glucose monitoring is introduced. The sensor divides six CSRR units into two parallel circuits, forming 2 × 3 CSRR matrix to expand the sensing area of the sensor. This sensor can adjust the resonant frequency of the sensor itself by adjusting the spacing between the horizontal or vertical CSRR units. In this paper, the sensor is achieved a resonance frequency of 2.5–3.5 GHz after loading glucose solution. When the concentration of the solution changes, the resonance frequency and S21 depth of the resonator changes with the dielectric parameters of the solution. Thus, non-invasive monitoring of glucose concentration is achieved. The variation relationship between resonant frequency and CSRR unit spacing and the feasibility of using it for solution detection are verified through simulation. The experimental results are showed that the resonant frequency sensitivity of the sensor is 9.59 × 10−2 MHz/(mg/dL), and the |S21| sensitivity is 7.47 × 10−3 dB/(mg/dL).

Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose.

Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose. In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy. A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone. The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.

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.

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.

A Non-invasive Blood Glucose Detection System Based on Photoplethysmogram with Multiple Near-infrared Sensors.

Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0.242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.

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

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

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.

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.

Noninvasive glucose monitoring using portable GOx-Based biosensing system

Wearable biosensors have gained huge interest due to their potential for real-time physiological information. The development of a non-invasive blood glucose device is of great interests for health monitoring in reducing the diabetes incidence. Here, we report a sandwich-structured biosensor that is designed for glucose levels detection by using sweat as the means of monitoring. The Prussian blue nanoparticles (PBNPs) and carboxylated carbon nanotubes (MWCNT-COOH) were self-assembled on the electrode to improve the electrochemical performance and as the sensor unit, glucose oxidase (GOx) was immobilized by chitosan (CS) as the reaction catalysis unit, and finally encapsulated with Nafion to ensure a stable performance. As a result, the GOx/PBNPs/MWCNT-COOH sensor displays a low detection limit (7.0 μM), high sensitivity (11.87 μA mM−1 cm−2), and excellent interference resistance for a full sweat glucose application range (0.0–1.0 mM) for both healthy individuals and diabetic patients. Additionally, the glucose sensor exhibits stable stability for two weeks and can be successfully applied to screen-printed carbon electrodes (SPCE), demonstrating its great potential for personalized medical detection and chronic disease management.

Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography.

To reduce the error induced by overfitting or underfitting in predicting non-invasive fasting blood glucose (NIBG) levels using photoplethysmography (PPG) data alone, we previously demonstrated that incorporating HbA1c led to a notable 10% improvement in NIBG prediction accuracy (the ratio in zone A of Clarke's error grid). However, this enhancement came at the cost of requiring an additional HbA1c measurement, thus being unfriendly to users. In this study, the enhanced HbA1c NIBG deep learning model (blood glucose level predicted from PPG and HbA1c) was trained with 1494 measurements, and we replaced the HbA1c measurement (explicit HbA1c) with "implicit HbA1c" which is reversely derived from pretested PPG and finger-pricked blood glucose levels. The implicit HbA1c is then evaluated across intervals up to 90 days since the pretest, achieving an impressive 87% accuracy, while the remaining 13% falls near the CEG zone A boundary. The implicit HbA1c approach exhibits a remarkable 16% improvement over the explicit HbA1c method by covering personal correction items automatically. This improvement not only refines the accuracy of the model but also enhances the practicality of the previously proposed model that relied on an HbA1c input. The nonparametric Wilcoxon paired test conducted on the percentage error of implicit and explicit HbA1c prediction results reveals a substantial difference, with a p-value of 2.75 × 10-7.

Review of Noninvasive Continuous Glucose Monitoring in Diabetics.

For diabetics, taking regular blood glucose measurements is crucial. However, traditional blood glucose monitoring methods are invasive and unfriendly to diabetics. Recent studies have proposed a biofluid-based glucose sensing technique that creatively combines wearable devices with noninvasive glucose monitoring technology to enhance diabetes management. This is a revolutionary advance in the diagnosis and management of diabetes, reflects the thoughtful modernization of medicine, and promotes the development of digital medicine. This paper reviews the research progress of noninvasive continuous blood glucose monitoring (CGM), with a focus on the biological liquids that replace blood in monitoring systems, the technical principles of continuous noninvasive glucose detection, and the output and calibration of sensor signals. In addition, the existing limits of noninvasive CGM systems and prospects for the future are discussed. This work serves as a resource for further promoting the development of noninvasive CGM systems.

Telemedicine and AI in Remote Patient Monitoring

This article explores how telemedicine, especially with the help of artificial intelligence (AI), is transforming healthcare. It covers its applications in monitoring patients, managing chronic diseases like diabetes, and improving cardiovascular care. The importance of wearable devices and non-invasive blood glucose monitoring is highlighted. The article emphasizes how AI-driven remote patient monitoring can enhance healthcare by providing early intervention, reducing hospitalizations, and offering personalized care

Generative adversarial networks‐based super‐resolution algorithm enables high signal‐to‐noise ratio spatial heterodyne Raman spectra

AbstractHigh‐resolution interference pattern images are vital for spatial heterodyne Raman spectroscopy to produce quality Raman spectra with a good signal‐to‐noise ratio. A sought‐after super‐resolution algorithm can enhance Raman interference pattern images, reducing reliance on expensive imaging sensors. This enables miniaturization and portability for point‐of‐care testing. This study proposes a generative adversarial network (GAN)‐based super‐resolution reconstruction algorithm explicitly designed for Raman interference patterns. Employing GAN adversarial training, the algorithm effectively reconstructs interference pattern images at higher resolution, improving Raman spectra signal‐to‐noise ratio. Considering Raman spectra analysis mainly focuses on characteristic peaks, a Raman characteristic peak‐focused network training scheme is used. For instance, acetaminophen is studied with two selected Raman characteristic peaks centered at 388 and 858 cm−1. These peaks are observed in Fourier transformed Raman spectra from corresponding low‐resolution interference pattern images obtained by down‐sampling high‐resolution ones by twofold and fourfold. The proposed GAN‐based algorithm successfully reconstructs low‐resolution interference patterns into high‐resolution ones, achieving high R‐square values (96.05% for twofold and 91.1% for fourfold). This innovation holds potential for point‐of‐care applications, like noninvasive blood glucose concentration measurements, enabling cost‐effective, portable Raman spectrometers with improved capabilities.

Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation

Non-invasive blood glucose estimation is an extensively researched area since current gold-standard invasive glucose monitoring methods present numerous inconveniences and challenges in terms of comfort and cost. We present the design, development, and validation of an Internet of Medical Things (IoMT) based wearable device for non-invasive and real-time measurement of blood glucose. This paper presents a diabetic health monitoring platform architecture that consists of (a) a user-worn photoplethysmography (PPG) device, (b) a smart analytics cloud that deploys models for blood glucose estimation, and (c) an end-to-end mobile/web application for monitoring diabetes patients. Blood glucose computation is achieved using a novel light-weight 1-dimensional input-reinforced deep neural network architecture, which we call as GlucoNet. This captures both long and short, temporal and spatial features from the PPG signal. The training and validation of the model were conducted on a dataset of 283 participants which demonstrated a mean absolute percentage error (MAPE) of 17.8% (± 12.8%) wherein 100% of predictions fall in the clinically acceptable zones A and B of the Clarke-error grid. The lightweight model is also deployed on edge devices for real-time and offline blood glucose measurement. We report a clinical outcome deployment study and insights from 20,000＋ glucose measurements obtained from another 600 patients. To our knowledge, this is the largest reported work employing a non-calibrated, non-invasive, demography, and time-of-food agnostic IoMT glucose monitoring system that does not require any feature engineering and is capable of running on edge devices.

OPTICAL BASED NON INVASIVE GLUCOMETER WITH IOT

Diabetes mellitus (DM) nicknamed as sugar is becoming one of incurable and critical challenge to medical field. Serious illness such as premature mortality even cause of death, which can be identified by continuous monitoring from a remote location. The traditional and conventional method such as finger pricking's demerits such as pain and damage to tissues that cause infection. Such type of infections can be overcome by non-invasive technique. The objective of this paper is to monitor glucose level using non-invasive technique by optical and IoT technology. The proposed sensor circuit consists of IR LED's of wavelength 650–2500nm for optical blood glucose measurement and NIR photodiodes (InGaAs) to receive the reflected light from body parts to determine the glucose level. The Beer-Lambert law is used for signal processing along with WIFI based IoT real time information transmission. The project is implemented using Arduino IDE for finding the performance matrix of the system as well as various analytical studies. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market. Key Words: Diabetes mellitus, blood glucose measurement, WIFI based IoT, wearable technology

Enhanced Acetone Sensing at Room Temperature using Tailored Co3O4 Nanostructures-Coated Optical Fibers: An Application towards Non-invasive Blood Glucose Sensor

Detection of acetone molecules is of paramount importance in the fields of healthcare, air quality monitoring and industrial uses. Herein, we proposed a room temperature Co3O4 nanoparticles coated optical fiber acetone gas sensor. Structural, morphological and uniformity coating of synthesized Co3O4 nanoparticles were investigated using X-ray diffraction and electron microscopy techniques. Interestingly, sensing studies uncovered the linear characteristic in output detector current with change in 0-2.5 ppm acetone concentration for all the sensors (named as F-S350, F-S500 and F-S1000 sensors coated with nanoparticles annealed at 350, 500 and 1000°C, respectively). The F-S350 sensor divulged a significant increase in output current, sensitivity around 15 times higher and reduced limit-of-detection (LoD) values with regard to F-S1000 device. Furthermore, the sensing parameters were augmented by exposed to lower acetone concentration. The experimental findings established the F-S350 sensor as a superior device, which is explained by the evanescent field distribution outside coating region of fiber through FDTD simulation. Typically, the concentration of acetone gas in diabetic patients is above 1.8 ppm than healthy people (range 0.3-0.9 ppm), and therefore the proposed room temperature F- S350 device having LoD value 0.15 ppm can be deemed as a biomarker for the detection of acetone molecules in diabetic patients.