Non-invasive Blood Glucose Research Articles

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study.

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.

Signal Acquisition Methods for Vital and Nonvital Parameters in Electronic Health Devices: A Scoping Review

This paper presents the most common vital and nonvital parameters in current wearable electronic devices, describing the respective signal acquisition methods. We included complete studies in indexed sources and accessible online in the consulted databases between the years 2017 to 2022. Nine studies focused on capturing at least one vital sign. Five addressed HR, four FR, two BP, four SpO2, and seven body temperature. As for nonvital parameters, half of the total sample approached gait analysis, while one-third addressed non-invasive blood glucose sensors. The preparation of this material aimed to aid the scientific community by summarizing essential information to foster the development of new technologies capable of improving health decisions.

Non-invasive method for blood glucose monitoring using ECG signal

Abstract Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters. Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal. Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper. Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations.

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

Assessment of Finger Fat Pad Effect on CSRR-Based Sensor Scattering Parameters for Non-Invasive Blood Glucose Level Detection.

This paper examines the effect of finger fat pad thickness on the accuracy performance of complementary split-ring resonator (CSRR)-based microwave sensors for non-invasive blood glucose level detection. For this purpose, a simplified four-layer Cole-Cole model along with a CSRR-based microwave sensor have been comprehensively analyzed and validated through experimentation. Computed scattering parameter (S-parameter) responses to different fat layer thicknesses are employed to verify the concordance of the studied model with the measurement results. In this respect, a figure of merit (FM) based on the normalized squared difference is introduced to assess the accuracy of the considered Cole-Cole model. We have demonstrated that the analyzed model agrees closely with the experimental validation. In fact, the maximum error difference for all five fingertips does not exceed 1.73 dB over the entire frequency range of interest, from 1 GHz to 4 GHz.

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

Electromagnetic Wave Sensors for Noninvasive Blood Glucose Monitoring: Review and Recent Developments

Electromagnetic Wave Sensors for Noninvasive Blood Glucose Monitoring: Review and Recent Developments

Non-invasive blood glucose sensors based on electromagnetic wave

Non-invasive blood glucose sensors based on electromagnetic wave

2D bimetallic organic framework nanosheets for high-performance wearable power source and real-time monitoring of glucose.

Diabetics often prick their fingertips to measure the glucose levels in their blood. However, this traditional method not only causes prolonged pain but also increases the risk of infection. Hence, in this study, a non-invasive flexible glucose biosensor with high sensitivity was fabricated. Specifically, NiCo metal-organic frameworks (NiCo-MOFs) served as the electrode material of a micro-supercapacitor and sensing material of a glucose sensor. The electrochemical tests verified that the prominent sensitivity of the NiCo bimetal product is 1422.2 μA mM-1 cm-2. The micro-supercapacitor based on the as-fabricated NiCo-MOFs showed a high energy density of 11.5 mW h cm-2 at the power density 0.26 mW cm-2. In addition, the as-designed glucose device exhibited an excellent sensitivity of 0.31 μA μM-1. Furthermore, a flexible energy storage and glucose detection system was successfully prepared by further integrating the micro-supercapacitor and glucose sensor. The smart detector could accurately and conveniently measure the glucose concentration in sweat in real-time. Therefore, the wearable real-time sensing device displays feasible application for non-invasive glucose monitoring and health management.

Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.

Mid-infrared spectroscopy with an effective variable selection method based on MPA for glucose detection

Mid-infrared absorption spectroscopy is one of the most promising non-invasive blood glucose detection technologies. However, subsistent blood glucose detectors based on mid-infrared absorption spectroscopy technology are incapable of meeting the requirement of superior accuracy. Aimed at improving the prediction performance of glucose concentration in mid-infrared (MIR) spectra, an effective variable selection method (EVS) is proposed that generates the importance index for wavenumber to reduce the time cost. Tournament selection is then applied to avoid the reduction of exploration ability. L1 regularization is finally performed to tackle overfitting. 66 variables have been retained in the glucose fingerprint spectra by the proposed method, and the corresponding R and RMSEP are 0.93 and 32.22 mg/dL, respectively. Experimental results show that the proposed method outperforms five alternative approaches for variable selection from relevant literature, which is more accurate and robust.

Flexible enzymatic biosensor based on graphene sponge for glucose detection in human sweat

The development of flexible glucose biosensors makes it possible to perform non-invasive blood glucose monitoring through wearable devices, which is of great significance for the prevention and treatment of diabetes. In this study, a flexible GOx/CTS/GS/PB (GCGP) biosensor for sweat glucose detection was fabricated, which is comprised of gold electrode, Prussian blue (PB), graphene sponge (GS) chitosan (CTS) and glucose oxidase (GOx). The GCGP biosensor realizes a high detection sensitivity of 1790 nA·mM−1·cm−2 and a low detection limit of 2.45 μM (S/N = 3) towards sweat glucose at a low potential (0.075 V, vs. Ag/AgCl) in a linear range from 8.17 to 1000 μM. Moreover, the GCGP biosensor shows high selectivity for glucose and has a good response to glucose in real sweat, which was collected from volunteers playing table tennis. The superior performance of flexible sweat glucose sensors can be due in no small part to the addition of GS. The large specific surface area and the cross-linked hierarchical porous structure of GS enable easy absorption and even distribution of a large amount of GOx. Combined with the high conductivity inherited from graphene, multiple channels for the rapid transport of electrons and ions are available. The abundant binding sites of GS provide the possibility for further composites with other functional materials to improve the performance of the flexible sweat glucose biosensor in future research.

Glucose emission spectra through mid-infrared passive spectroscopic imaging of the wrist for non-invasive glucose sensing

Non-invasive blood glucose sensing can be achieved using mid-infrared spectroscopy, although no practical device based on this method has yet been developed. Here, we propose mid-infrared passive spectroscopic imaging for glucose measurements from a distance. Spectroscopic imaging of thermal radiation from the human body enabled, for the first time in the world, the detection of glucose-induced luminescence from a distance. In addition, glucose emission spectra of the wrist acquired at regular intervals up to 60 min showed that there was a strong correlation between the glucose emission intensity and blood glucose level measured using an invasive sensor. Thus, the new technology proposed here is expected to be applied to real-time monitoring of diabetic patients to detect hypoglycemic attacks during sleep and to detect hyperglycemia in a population. Moreover, this technology could lead to innovations that would make it possible to remotely measure a variety of substances.

Basic Study on Measurement of Return Loss and Smith Chart Change Using Microstrip Patch Antenna with Concentration Transition for Non-invasive Blood Glucose Measurement.

This paper describes a basic study on the measurement of return loss and change in Smith chart using a microstrip patch antenna (MPA) with concentration transition to perform non-invasive blood glucose measurements. To evaluate blood glucose level changes in the human body, the concentration of measurements was changed 10 times to an equivalent of 100mg/dL in a range of 0-1000mg/dL to reflect a concentration of 400mg/dL, which is the fatal level of diabetes. Five types of MPAs were fabricated that formed resonant frequencies in the 1, 2, 3, 4, and 5GHz bands, and were used in the experiment. Each MPA constituted a sharp narrow band characteristic and induced a large change in return loss. By measuring the return loss and Smith chart to evaluate the concentration change at resonant frequencies, the return loss was observed to change by an average of 0.058 dB for every 100mg/dL change, and the impedance magnitudes and phase angles analyzed through the Smith chart were observed to have a certain tendency, which confirmed that they were changed. This study shows that for performing non-invasive measurements of blood sugar level, measuring the change in return loss can provide a more stable and reliable measurement compared to the two methods that are simultaneously used to view the samples.

A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management.

The current standard of diabetes management depends upon the invasive blood pricking techniques. In recent times, the availability of minimally invasive continuous glucose monitoring devices have made some improvements in the life of diabetic patients however it has its own limitations which include painful insertion, excessive cost, discomfort and an active risk due to the presence of a foreign body under the skin. Due to all these factors, the non-invasive glucose monitoring has remain a subject of research for the last two decades and multiple techniques of non-invasive glucose monitoring have been proposed. These proposed techniques have the potential to be evolved into a wearable device for non-invasive diabetes management. This paper reviews research advances and major challenges of such techniques or methods in recent years and broadly classifies them into four types based on their detection principles. These four methods are: optical spectroscopy, photoacoustic spectroscopy, electromagnetic sensing and nanomaterial based sensing. The paper primarily focuses on the evolution of non-invasive technology from bench-top equipment to smart wearable devices for personalized non-invasive continuous glucose monitoring in these four methods. With the rapid evolve of wearable technology, all these four methods of non-invasive blood glucose monitoring independently or in combination of two or more have the potential to become a reality in the near future for efficient, affordable, accurate and pain-free diabetes management.

Мониторинг уровня глюкозы крови: возможности современных глюкометров.

Background. Diabetes mellitus is one of the key causes of mortality in the world. Continuous monitoring of blood glucose levels is necessary for the correct drug treatment and complication risk prediction. Materials and methods. The article examines all the blood glucose monitoring systems in the world available in September 2022, as well as promising developments. There are used available literary sources, Internet resources, including websites of producing companies and startups. Results. Most of the non-invasive glucose meters considered in the article are the devices prototypes. They are at the stage of testing and hypothesis testing. Teams from different countries use different methods and look for patterns based on information obtained from currently available sensors that allow you to determine a blood glucose level at a particular moment. Despite the use of the latest technologies, many companies are unable to obtain consistently accurate results. Conclusions. In the near future flash monitoring systems in their new generations will become increasingly widespread and recognized by real patient users. The ability to track data in continuous mode allows you to talk about the chronic disease management. The prospect of creating a completely non-invasive blood glucose meter attracts many development teams around the world. However, technological difficulties in development delay a product creating process. Many projects are completed without getting the expected result. Now, there is no non-invasive glucose meter that meets all the clinical medicine requirements, providing complete and accurate control of blood glucose levels.

Glucose Determination by a Single 1535 nm Pulsed Photoacoustic Technique: A Multiple Calibration for the External Factors.

Photoacoustic spectroscopy has been proved to be a potential method for noninvasive blood glucose detection. We used 1535 nm pulsed laser to excite photoacoustic signal in glucose solution and then explored the influence of different glucose concentration on photoacoustic signal to analyze the sensitivity of photoacoustic signal to glucose at this wavelength. We designed a simple photoacoustic cell structure, which used a focused ultrasonic transducer to receive signals, so as to reduce signal attenuation. In terms of the results, we have found that for high-concentration glucose solutions, the results have strong linearity and discrimination, and when the concentration is close to the human body level, the signal difference is not so obvious. Therefore, we explore the external factors affecting the photoacoustic signal in detail and propose a calibration method. Through calibration, the signal generated by the low-concentration glucose solution also has a good linearity.

Non-invasive blood glucose sensing by machine learning of optic fiber-based speckle pattern variation.

.SignificanceThe ability to perform frequent non-invasive monitoring of glucose in the bloodstream is very applicable for diabetic patients.AimWe experimentally verified a non-invasive multimode fiber-based technique for sensing glucose concentration in the bloodstream by extracting and analyzing the collected speckle patterns.ApproachThe proposed sensor consists of a laser source, digital camera, computer, multimode fiber, and alternating current (AC) generated magnetic field source. The experiments were performed using a covered (with cladding and jacket) and uncovered (without cladding and jacket) multimode fiber touching the skin under a magnetic field and without it. The subject’s finger was placed on a fiber to detect the glucose concentration. The method tracks variations in the speckle patterns due to light interaction with the bloodstream affected by blood glucose.ResultsThe uncovered fiber placed above the finger under the AC magnetic field (150 G) at 140 Hz was found to have a lock-in amplification role, improving the glucose detection precision. The application of the machine learning algorithms in preprocessed speckle pattern data increase glucose measurement accuracy. Classification of the speckle patterns for uncovered fiber under the AC magnetic field allowed for detection of the blood glucose with high accuracy for all tested subjects compared with other tested configurations.ConclusionsThe proposed technique was theoretically analyzed and experimentally validated in this work. The results were verified by the traditional finger-prick method, which was also used for classification as a conventional reference marker of blood glucose levels. The main goal of the proposed technique was to develop a non-invasive, low-cost blood glucose sensor for easy use by humans.

Research on multi-parameter fusion non-invasive blood glucose detection method based on machine learning.

Traditional blood glucose testing methods have several disadvantages, such as high pain and poor acquisition continuity. In response to these shortcomings, we propose a multi-parameter fusion non-invasive blood glucose detection method that combines machine learning and photoplethysmography (PPG) signal feature parameter analysis. This method uses the signal validity check process based on the correlation operation to test and calculate PPG data. It, then, respectively applies the bootstrap aggregation algorithm and the random forests algorithm to establish two non-invasive blood glucose detection models that comprehensively predict blood glucose data. Experimental comparative analysis showed that the accuracy of the detection model based on the random forests algorithm is superior. The correlation coefficient of the obtained blood glucose prediction set is 0.972, the mean square error is 0.257, and the relative error is less than ± 20%. Relative error in blood glucose prediction meets the national standards in China. Meanwhile, the results of the Clarke Error Grid Analysis indicate that the non-invasive blood glucose testing method proposed in this study meets clinical accuracy requirements.

OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV)

High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier. Outliers are part of observations that do not follow the data distribution pattern and are located far from the data center. The existence of outliers needs to be detected because it can lead to deviations from the analysis results. One of the methods used to detect outliers is the Mahalanobis distance. To obtain a robust Mahalanobis distance, the Minimum Vector Variance (MVV) method is used. This study will compare the MVV method with the classical Mahalanobis distance method in detecting outliers in non-invasive blood glucose level data, both at p>n and n>p. The test results show that the MVV method is better for n>p. MVV shows more effective results in identifying the minimum data group and outlier data points than the classical method.