Non-invasive glucose monitoring in patients with Type 1 diabetes: A Multisensor system combining sensors for dielectric and optical characterisation of skin

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R 2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ( m g / d l ) 2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Noninvasive, wearable, and tunable electromagnetic multisensing system for continuous glucose monitoring, mimicking vasculature anatomy

Painless, needle-free, and continuous glucose monitoring sensors are needed to enhance the life quality of diabetic patients. To that extent, we propose a first-of-its-kind, highly sensitive, noninvasive continuous glycemic monitoring wearable multisensor system. The proposed sensors are validated on serum, animal tissues, and animal models of diabetes and in a clinical setting. The noninvasive measurement results during human trials reported high correlation (>0.9) between the system's physical parameters and blood glucose levels, without any time lag. The accurate real-time responses of the sensors are attributed to their unique vasculature anatomy-inspired tunable electromagnetic topologies. These wearable apparels wirelessly sense hypo- to hyperglycemic variations with high fidelity. These components are designed to simultaneously target multiple body locations, which opens the door for the development of a closed-loop artificial pancreas.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels

This paper reviews non-invasive blood glucose measurements via dielectric spectroscopy at microwave frequencies presented in the literature. The intent is to clarify the key challenges that must be overcome if this approach is to work, to suggest some possible ways towards addressing these challenges and to contribute towards prevention of unnecessary ‘reinvention of the wheel’.

Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement

Non-invasive Glucose Measurement (NGM) technology makes great sense for the blood glucose management of patients with hyperglycemia or hypoglycemia. Individual Discrepancies (IDs), e.g., skin thickness and color, not only block the development of NGM, but also become the reason why NGM cannot be widely used. To solve this problem, our solution is designing an individual customized NGM model that can measure these discrepancies through multi-wavelength and tune parameters for glucose estimating. In this paper, an NGM prototype is designed, and a learning model for glucose estimating with automatically parameters tuning based on Independent Component Analysis (ICA) and Random Forest (RF) is presented. The clinic trial proves that the correlation coefficient between estimation and reference Blood Glucose Concentration (BGC) can reach 0.5 after merely 10 times of learning, and rise to 0.8 after about 60 times of learning.

Real-Time Monitoring of Tetraselmis suecica in A Saline Environment as Means of Early Water Pollution Detection

Biological water pollution, including organic pollutants and their possible transportation, via biofouling and ballast water, has the potential to cause severe economic and health impacts on society and environment. Current water pollution monitoring methods are limited by transportation of samples to the laboratory for analysis, which could take weeks. There is an urgent need for a water quality monitoring technique that generates real-time data. The study aims to assess the feasibility of three sensing techniques to detect and monitor the concentrations of the model species Tetraselmis suecica in real-time using eleven samples for each method. Results showed UV-Vis spectrophotometer detected increasing concentration of Tetraselmis suecica with R² = 0.9627 and R² = 0.9672, at 450 nm and 650 nm wavelengths, respectively. Secondly, low-frequency capacitance measurements showed a linear relationship with increasing concentration of Tetraselmis suecica at 150 Hz (R² = 0.8463) and 180 Hz (R² = 0.8391). Finally, a planar electromagnetic wave sensor measuring the reflected power S₁₁ amplitude detected increasing cell density at 4 GHz (R² = 0.8019).

Challenges and perspectives in continuous glucose monitoring

Diabetes is a global epidemic that threatens the health and well-being of hundreds of millions of people. The first step in patient treatment is to monitor glucose levels. Currently this is most commonly done using enzymatic strips. This approach suffers from several limitations, namely it requires a blood sample and is therefore invasive, the quality and the stability of the enzymatic strips vary widely, and the patient is burdened by performing the measurement themselves. This results in dangerous fluctuations in glucose levels often going undetected. There is currently intense research towards new approaches in glucose detection that would enable non-invasive continuous glucose monitoring (CGM). In this review, we explore the state-of-the-art in glucose detection technologies. In particular, we focus on the physical mechanisms behind different approaches, and how these influence and determine the accuracy and reliability of glucose detection. We begin by reviewing the basic physical and chemical properties of the glucose molecule. Although these play a central role in detection, especially the anomeric ratio, they are surprisingly often overlooked in the literature. We then review state-of-the art and emerging detection methods. Finally, we survey the current market for glucometers. Recent results show that past challenges in glucose detection are now being overcome, thereby enabling the development of smart wearable devices for non-invasive continuous glucose monitoring. These new directions in glucose detection have enormous potential to improve the quality of life of millions of diabetics, as well as offer insight into the development, treatment and even prevention of the disease.

First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHODS

To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient.

RESULTS

Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase.

CONCLUSIONS

The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHOD

To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively.

RESULTS

The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR.

CONCLUSIONS

The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

Noninvasive Continuous Glucose Monitoring Using a Multisensor-Based Glucometer and Time Series Analysis

Daily continuous glucose monitoring is very helpful in the control of glucose levels for people with diabetes and impaired glucose tolerance. In this study, a multisensor-based, noninvasive continuous glucometer was developed, which can continuously estimate glucose levels via monitoring of physiological parameter changes such as impedance spectroscopy at low and high frequency, optical properties, temperature and humidity. Thirty-three experiments were conducted for six healthy volunteers and three volunteers with diabetes. Results showed that the average correlation coefficient between the estimated glucose profiles and reference glucose profiles reached 0.8314, with a normalized root mean squared error (NRMSE) of 14.6064. The peak time of postprandial glucose was extracted from the glucose profile, and its estimated value had a correlation coefficient of 0.9449 with the reference value, wherein the root mean square error (RMSE) was 6.8958 min. Using Clarke error grid (CEG) analysis, 100% of the estimated glucose values fell in the clinically acceptable zones A and B, and 92.86% fell in zone A. The application of a multisensor-based, noninvasive continuous glucometer and time series analysis can endure the time delay between human physiological parameters and glucose level changes, so as to potentially accomplish noninvasive daily continuous glucose monitoring.

Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System

Noninvasive blood glucose (NIBG) measurement technique has been explored for the last three decades to facilitate diabetes management. Photoplethysmogram (PPG) signal may be used to measure the variations in blood glucose concentration. However, the literature reveals that physiological perturbations such as temperature, skin moisture, and sweat lead to less accurate NIBG measurements. The task of minimizing the effect of these perturbations for accurate measurements is an important research area. Therefore, in the present work, galvanic skin response (GSR) and temperature measurements along with PPG were used to measure blood glucose noninvasively. The data extracted from the sensors were used to estimate blood glucose concentration with the help of two machine learning (ML) techniques, i.e., multiple linear regression (MLR) and artificial neural network (ANN). The accuracy of proposed multisensor system was evaluated by pairing and comparing noninvasive measurements with invasively measured readings. The study was performed on 50 nondiabetic subjects with body mass index (BMI) 27.3 ± 3 kg/m2. The results revealed that multisensor NIBG measurement system significantly improves mean absolute prediction error and correlation coefficient in comparison to the techniques reported in the literature.

A 9 MHz-2.4 GHz Fully Integrated Transceiver IC for a Microfluidic-CMOS Platform Dedicated to Miniaturized Dielectric Spectroscopy

This paper presents a fully integrated transceiver IC as part of a self-sustained, microfluidic-CMOS platform for miniaturized dielectric spectroscopy (DS) from MHz to GHz. Fabricated in AMS 0.35 μm 2P/4M RF CMOS, the transmitter (TX) part of the IC generates a single-tone sinusoidal signal with frequency tunability in the range of ~ 9 MHz-2.4 GHz to excite a three-dimensional (3D), parallel-plate, capacitive sensor with a floating electrode and 9 μL microfluidic channel for sample delivery. With a material-under-test (MUT) loaded into the sensor, the receiver (RX) part of the IC employs broadband frequency response analysis (bFRA) methodology to measure the amplitude and phase of the RF excitation signal after transmission through the sensor. A one-time, 6-point sensor calibration algorithm then extracts both the real and imaginary parts of the MUT complex permittivity, ϵr, from IC measurements of the sensor transmission characteristics in the voltage domain. The "sensor + IC" is fully capable of differentiating among de-ionized (DI) water, phosphate-buffered saline (PBS), and alcoholic beverages in tests conducted at four excitation frequencies of ∼ 50 MHz , 500 MHz, 1.5 GHz, and 2.4 GHz generated by the TX. Moreover, permittivity readings of PBS by the sensor interfaced with the IC at six excitation frequencies in the range of ~ 50 MHz-2.4 GHz are in excellent agreement (rms error of 1.7% (real) and 7.2% (imaginary)) with those from bulk-solution reference measurements by commercial benchtop equipment. The total power consumption of the IC is with 1.5 V (analog) and 3.3 V (digital) supplies.

The Effect of a Global, Subject, and Device-Specific Model on a Noninvasive Glucose Monitoring Multisensor System

BACKGROUND

We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate.

METHOD

Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived.

RESULTS

We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals.

CONCLUSIONS

We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.

Technology platforms for remote monitoring of vital signs in the new era of telemedicine

Driven by healthcare cost and home healthcare need, the development of remote monitoring technologies is poised to improve and revolutionize healthcare delivery and accessibility. This paper reviews the recent progress in the field of remote monitoring technologies that may have the potential to become the basic platforms for telemedicine. In particular, key techniques and devices for monitoring cardiorespiratory activity, blood pressure and blood glucose concentration are summarized and discussed. In addition, the US FDA approved remote vital signs monitoring devices currently available on the market are presented.

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

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

Stretchable, wireless sensors and functional substrates for epidermal characterization of sweat

This paper introduces materials and architectures for ultrathin, stretchable wireless sensors that mount on functional elastomeric substrates for epidermal analysis of biofluids. Measurement of the volume and chemical properties of sweat via dielectric detection and colorimetry demonstrates some capabilities. Here, inductively coupled sensors consisting of LC resonators with capacitive electrodes show systematic responses to sweat collected in microporous substrates. Interrogation occurs through external coils placed in physical proximity to the devices. The substrates allow spontaneous sweat collection through capillary forces, without the need for complex microfluidic handling systems. Furthermore, colorimetric measurement modes are possible in the same system by introducing indicator compounds into the depths of the substrates, for sensing specific components (OH(-) , H(+) , Cu(+) , and Fe(2+) ) in the sweat. The complete devices offer Young's moduli that are similar to skin, thus allowing highly effective and reliable skin integration without external fixtures. Experimental results demonstrate volumetric measurement of sweat with an accuracy of 0.06 μL/mm(2) with good stability and low drift. Colorimetric responses to pH and concentrations of various ions provide capabilities relevant to analysis of sweat. Similar materials and device designs can be used in monitoring other body fluids.

Accuracy of a Novel Noninvasive Multisensor Technology to Estimate Glucose in Diabetic Subjects During Dynamic Conditions

The purpose of this study was to determine whether an approach of multisensor technology with integrated data analysis in an armband system (SenseWear® Pro Armband, SWA) can provide estimates of plasma glucose concentration in diabetes. In all, 41 subjects with diabetes participated. On day 1 subjects underwent an oral glucose tolerance test (OGTT) and on day 2 a 60-minute treadmill test (TT). SWA plasma glucose estimates were compared against reference peripheral venous glucose concentrations. A continuous glucose monitoring device (CGM) was also placed on each subject to serve as a reference for clinical comparison. Pearson coefficient, Clarke error grid (CEG), and mean absolute relative difference (MARD) analyses were used to compare the performance of plasma glucose estimation. There were significant correlations between plasma glucose concentrations estimated by the SWA and the reference plasma glucose concentration during the OGTT (r = .65, P < .05) and the TT (r = .91, P < .05). CEG analysis revealed that during the OGTT, 93% of plasma glucose concentration readings were in the clinically acceptable zone A+B for the SWA and 95% for the CGM. During the TT, the SWA had 96% of readings in zone A+B, compared to 97% for the CGM. During OGTTs, MARDs for the SWA and CGM were 26% and 18%, respectively. During TTs, MARDs were 16% and 12%, respectively. Plasma glucose concentration estimation by the SWA's noninvasive multisensor approach appears to be feasible and its performance in estimating glucose approaches that of a CGM. The success of this pilot study suggests that multisensor technology holds promising potential for the development of a wearable, noninvasive, painless glucose monitor.

Non-invasive continuous glucose monitoring with multi-sensor systems: a Monte Carlo-based methodology for assessing calibration robustness

In diabetes research, non-invasive continuous glucose monitoring (NI-CGM) devices represent a new and appealing frontier. In the last years, some multi-sensor devices for NI-CGM have been proposed, which exploit several sensors measuring phenomena of different nature, not only for measuring glucose related signals, but also signals reflecting some possible perturbing processes (temperature, blood perfusion). Estimation of glucose levels is then obtained combining these signals through a mathematical model which requires an initial calibration step exploiting one reference blood glucose (RBG) sample. Even if promising results have been obtained, especially in hospitalized volunteers, at present the temporal accuracy of NI-CGM sensors may suffer because of environmental and physiological interferences. The aim of this work is to develop a general methodology, based on Monte Carlo (MC) simulation, to assess the robustness of the calibration step used by NI-CGM devices against these disturbances. The proposed methodology is illustrated considering two examples: the first concerns the possible detrimental influence of sweat events, while the second deals with calibration scheduling. For implementing both examples, 45 datasets collected by the Solianis Multisensor system are considered. In the first example, the MC methodology suggests that no further calibration adjustments are needed after the occurrence of sweat events, because the “Multisensor+model” system is able to deal with the disturbance. The second case study shows how to identify the best time interval to update the model's calibration for improving the accuracy of the estimated glucose. The methodology proposed in this work is of general applicability and can be helpful in making those incremental steps in NI-CGM devices development needed to further improve their performance.

Italian contributions to the development of continuous glucose monitoring sensors for diabetes management

Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community.

A 4-h hyperglycaemic excursion induces rapid and slow changes in major electrolytes in blood in healthy human subjects

Hyperglycaemia is well known to cause reductions in plasma Na(+) levels or even hyponatraemia due to an osmotically induced dilution of the interstitium and blood. It is, however, unclear whether this dilution is significantly counteracted by ion regulatory homeostatic mechanism(s) or not. Furthermore, the effects of moderate hyperglycaemia on other major ions are less well known. To further clarify these questions, we measured the changes in blood osmolarity and concentrations of Na(+), K(+), Cl(-), Mg(2+) and Ca(2+) during a 4-h-long experimental hyperglycaemia in healthy subjects rendered temporarily insulin deficient using the hyperglycaemic clamp. Hyperglycaemia, 16.8 mM, was rapidly imposed from a baseline of 4.4 mM by intravenous somatostatin and glucose infusions in 19 healthy subjects (10 m, 9 f; age 36 ± 5 years (mean ± SD); BMI 22.7 ± 2.9 kg/m(2)). Subsequently, glycaemia was returned to basal and measurements continued until all dynamic changes had stopped (at ~8 h). Osmolarity increased from 281.8 ± 0.7 to 287.9 ± 0.7, while Na(+) decreased from 143.9 ± 0.3 to 138.7 ± 0.2, Cl(-) from 101.7 ± 0.2 to 99.5 ± 0.1, Ca(2+) from 1.98 ± 0.04 to 1.89 ± 0.02 and Mg(2+) from 0.84 ± 0.01 to 0.80 ± 0.00 mM. All these changes were rapidly reaching stable levels. K(+) increased from 4.02 ± 0.02 to 4.59 ± 0.02 mM (P < 0.0001) also reaching stable levels but with some delay. Na(+), Cl(-), Mg(2+) and Ca(2+) are essentially determined by blood dilution, and their values will remain diminished as long as the hyperglycaemia lasts. Partial suppression of insulin-stimulated Na(+)/K(+) pumping lead to increased K(+) levels. The combination of elevated K(+) and decreased Mg(2+) and Ca(2+) levels may lead to an altered excitability, which is particularly relevant for diabetic patients with heart disease.

Recent advances in noninvasive glucose monitoring

The race for the next generation of painless and reliable glucose monitoring for diabetes mellitus is on. As technology advances, both diagnostic techniques and equipment improve. This review describes the main technologies currently being explored for noninvasive glucose monitoring. The principle of each technology is mentioned; its advantages and limitations are then discussed. The general description and the corresponding results for each device are illustrated, as well as the current status of the device and the manufacturer; internet references for the devices are listed where appropriate. Ten technologies and eleven potential devices are included in this review. Near infrared spectroscopy has become a promising technology, among others, for blood glucose monitoring. Although some reviews have been published already, the rapid development of technologies and information makes constant updating mandatory. While advances have been made, the reliability and the calibration of noninvasive instruments could still be improved, and more studies carried out under different physiological conditions of metabolism, bodily fluid circulation, and blood components are needed.

Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the Multisensor system

Non-invasive continuous glucose monitoring (NI-CGM) sensors are still at an early stage of development, but, in the near future, they could become particularly appealing in diabetes management. Solianis Monitoring AG (Zurich, Switzerland) has proposed an approach for NI-CGM based on a multi-sensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to estimate glucose levels from the 150 channels directly measured through the Multisensor. A static multivariate linear regression model (with order and parameters common to the entire population of subjects) was proposed for such a scope (Caduff et al., Biosens Bioelectron 26:3794-3800, 2011). The aim of this work is to evaluate the accuracy in the estimation of glucose levels and trends that the NI-CGM Multisensor platform can achieve by exploiting different techniques for model identification, namely, ordinary least squares, subset variable selection, partial least squares and least absolute shrinkage and selection operator (LASSO). Data collected in human beings monitored for a total of 45 study days were used for model identification and model test. Several metrics of standard use in the diabetes scientific community to measure point and clinical accuracy of glucose sensors were used to assess the models. Results indicate that the LASSO technique is superior to the others shrinking many channel weights to zero thus leading to smoother glucose profiles and resulting in a more robust model to possible artifacts in the Multisensor data. Although, as expected, the performance of the NI-CGM system with the LASSO model is not yet comparable with that of enzyme-based needle glucose sensors, glucose trends are satisfactorily estimated. Considering the non-invasive nature of the multi-sensor platform, this result can have an immediate impact in the current clinical practice, e.g., to integrate sparse self-monitoring of blood glucose data with an indication of the glucose trend to aid the diabetic patient in dealing with, or even preventing in the short time scale, the threats of critical events such as hypoglycaemia.

Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model

INTRODUCTION

Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system.

METHODS

Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated.

RESULTS

From our clinical study of 16 children with T1DM, natural occurrence of nocturnal-hypoglycemic episodes was associated with increased heart rates and increased corrected QT intervals. All the data sets were collected from the Government of Western Australia's Department of Health. All data were organized randomly into a training set (8 patients with 320 data points) and a testing set (another 8 patients with 269 data points). To prevent the phenomenon of overtraining, we separated the training set into 2 sets (4 patients in each set) and a fitness function was introduced for this training process. The testing performances of the proposed algorithm for detection of advanced hypoglycemic episodes (sensitivity=85.71% and specificity=79.84%) and hypoglycemic episodes (sensitivity=80.00% and specificity=55.14%) were given.

CONCLUSION

We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.

Dynamics of blood electrolytes in repeated hyper- and/or hypoglycaemic events in patients with type 1 diabetes

AIMS/HYPOTHESIS

Electrolyte disturbances are well-known consequences of the diabetic pathology. However, less is known about the cumulative effects of repeated changes in glycaemia, a characteristic of diabetes, on the electrolyte balance. We therefore investigated the ionic profiles of patients with type 1 diabetes during consecutive hyper- and/or hypoglycaemic events using the glucose clamp.

METHODS

In protocol 1, two successive hyperglycaemic excursions to 18 mmol/l were induced; in protocol 2, a hypoglycaemic excursion (2.5 mmol/l) was followed by a hyperglycaemic excursion (12 mmol/l) and another hypoglycaemic episode (3.0 mmol/l).

RESULTS

Blood osmolarity increased during hyperglycaemia and was unaffected by hypoglycaemia. Hyperglycaemia induced decreases in plasma Na(+) Cl(-) and Ca(2+) concentrations and increases in K(+) concentrations. These changes were faithfully reproduced during a second hyperglycaemia. Hypoglycaemia provoked rapid and rapidly reversible increases in Na(+), Cl(-) and Ca(2+). In sharp contrast, K(+) levels displayed a rapid and substantial fall from which they did not fully recover even 2 h after the re-establishment of euglycaemia. A second hypoglycaemia caused an additional fall.

CONCLUSIONS/INTERPRETATION

Repeated hyperglycaemia events do not lead to any cumulative effects on blood electrolytes. However, repeated hypoglycaemias are cumulative with respect to K(+) levels due to a very slow recovery following hypoglycaemia. These results suggest that recurring hypoglycaemic events may lead to progressively lower K(+) levels despite rapid re-establishment of euglycaemia. This warrants close monitoring of plasma K(+) levels combined with continuous glucose monitoring particularly in patients under intensive insulin therapy who are subject to repeated hypoglycaemic episodes.

TRIAL REGISTRATION

Clinicaltrial.gov NCT01060917.

Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?

Closed-loop (automated) controllers are encountered in all aspects of modern life in applications ranging from air-conditioning to spaceflight. Although these systems are virtually ubiquitous, they are infrequently used in anesthesiology because of the complexity of physiologic systems and the difficulty in obtaining reliable and valid feedback data from the patient. Despite these challenges, closed-loop systems are being increasingly studied and improved for medical use. Two recent developments have made fluid administration a candidate for closed-loop control. First, the further description and development of dynamic predictors of fluid responsiveness provides a strong parameter for use as a control variable to guide fluid administration. Second, rapid advances in noninvasive monitoring of cardiac output and other hemodynamic variables make goal-directed therapy applicable for a wide range of patients in a variety of clinical care settings. In this article, we review the history of closed-loop controllers in clinical care, discuss the current understanding and limitations of the dynamic predictors of fluid responsiveness, and examine how these variables might be incorporated into a closed-loop fluid administration system.

A dielectric inverse problem applied to human skin measurements during glucose excursions

A fringing field capacitive sensor has been used to measure the dielectric properties of human skin and underlying tissue in the MHz frequency range. It has recently been shown in clinical experimental studies that these dielectric properties can be related to the effects of in vivo glucose variations of the test subject. Previously, the relationship between electrical impedance and the glucose level has been established via statistical methods, such as the regression method. In this work, we explored a different approach, namely the resolution of the so-called inverse problem. First we applied the method on an artificial two-layer lossy system in order to test the sensitivity of the solution to forced changes in the layer properties and its stability to a constant setting. After validation of this method on artificial systems, a similar inverse problem was set and solved for dielectric measurements on human skin during an induced glucose excursion, where the skin is also modelled as a double-layer system. The changes of the measured permittivity and conductivity of the second layer versus the glucose changes are calculated for 22 study days. The statistical distribution shows that the median slopes of both dielectric properties are negative. These results can be used to test our hypothesis and to continue building potential explanations for the phenomena induced by the glucose changes on the skin layer dielectric parameters.

Data processing for noninvasive continuous glucose monitoring with a multisensor device

BACKGROUND

Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue.

METHOD

Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike's information criterion. In order to model blood glucose, a linear least-squares model was used. A Monte Carlo simulation was applied to examine the effects of personalizing models.

RESULTS

The principal component analysis was able to identify two major effects in the impedance spectra: a blood glucose-related process and an equilibration process related to moisturization of the skin and underlying tissue. With a global linear least-squares model, a coefficient of determination (R²) of 0.60 was achieved, whereas the personalized model reached an R² of 0.71. The Monte Carlo simulation proved a significant advantage of personalized models over global models.

CONCLUSION

A principal component analysis is useful for extracting glucose-related effects in the impedance spectra of human skin. A linear global model based on Solianis Multisensor data yields a good predictive power for blood glucose estimation. However, a personalized linear model still has greater predictive power.

Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation

The Multisensor Glucose Monitoring System (MGMS) features non invasive sensors for dielectric characterisation of the skin and underlying tissue in a wide frequency range (1 kHz-100 MHz, 1 and 2 GHz) as well as optical characterisation. In this paper we describe the results of using an MGMS in a miniaturised housing with fully integrated sensors and battery. Six patients with Type I Diabetes Mellitus (age 44±16 y; BMI 24.1±1.3 kg/m(2), duration of diabetes 27±12 y; HbA1c 7.3±1.0%) wore a single Multisensor at the upper arm position and performed a total of 45 in-clinic study days with 7 study days per patient on average (min. 5 and max. 10). Glucose changes were induced either orally or by i.v. glucose administration and the blood glucose was measured routinely. Several prospective data evaluation routines were applied to evaluate the data. The results are shown using one of the restrictive data evaluation routines, where measurements from the first 22 study days were used to train a linear regression model. The global model was then prospectively applied to the data of the remaining 23 study days to allow for an external validation of glucose prediction. The model application yielded a Mean Absolute Relative Difference of 40.8%, a Mean Absolute Difference of 51.9 mg dL(-1), and a correlation of 0.84 on average per study day. The Clarke error grid analyses showed 89.0% in A+B, 4.5% in C, 4.6% in D and 1.9% in the E region. Prospective application of a global, purely statistical model, demonstrates that glucose variations can be tracked non invasively by the MGMS in most cases under these conditions.

Time-Varying Procedures for Insulin-Dependent Diabetes Mellitus Control

This work considers the problem of automatically controlling the glucose level in insulin dependent diabetes mellitus (IDDM) patients. The objective is to include several important and practical issues in the design: model uncertainty, time variations, nonlinearities, measurement noise, actuator delay and saturation, and real time implementation. These are fundamental issues to be solved in a device implementing this control. Two time-varying control procedures have been proposed which take into consideration all of them: linear parameter varying (LPV) and unfalsified control (UC). The controllers are implemented with low-order dynamics that adapt continuously according to the glucose levels measured in real time in one case (LPV) and by controller switching based on the actual performance in the other case (UC). Both controllers have performed adequately under all these practical restrictions, and a discussion on pros and cons of each method is presented at the end.

Assessment of linear regression techniques for modeling multisensor data for non-invasive continuous glucose monitoring

New scenarios in diabetes treatment have been opened in the last ten years by continuous glucose monitoring (CGM) sensors. In particular, Non-Invasive CGM sensors are particularly appealing, even though they are still at an early stage of development. Solianis Monitoring AG (Zürich, Switzerland) has proposed an approach based on a multisensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to reconstruct the glucose concentration from the 150 channels measured with the device. Assuming a multivariate linear regression model (valid and usable for different individuals), the aim of this paper is the assessment of some techniques usable for determining such a model, namely Ordinary Least Squares (OLS), Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO). Once the model is identified on a training set, the accuracy of prospective glucose profiles estimated from "unseen" multisensor data is assessed. Preliminary results obtained from 18 in-clinic study days show that sufficiently accurate reconstruction of glucose levels can be achieved if suitable model identification techniques, such as LASSO, are considered.

Detecting vital signs with wearable wireless sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

Technologies for continuous glucose monitoring: current problems and future promises

Devices for continuous glucose monitoring (CGM) are currently a major focus of research in the area of diabetes management. It is envisioned that such devices will have the ability to alert a diabetes patient (or the parent or medical care giver of a diabetes patient) of impending hypoglycemic/hyperglycemic events and thereby enable the patient to avoid extreme hypoglycemic/hyperglycemic excursions as well as minimize deviations outside the normal glucose range, thus preventing both life-threatening events and the debilitating complications associated with diabetes. It is anticipated that CGM devices will utilize constant feedback of analytical information from a glucose sensor to activate an insulin delivery pump, thereby ultimately realizing the concept of an artificial pancreas. Depending on whether the CGM device penetrates/breaks the skin and/or the sample is measured extracorporeally, these devices can be categorized as totally invasive, minimally invasive, and noninvasive. In addition, CGM devices are further classified according to the transduction mechanisms used for glucose sensing (i.e., electrochemical, optical, and piezoelectric). However, at present, most of these technologies are plagued by a variety of issues that affect their accuracy and long-term performance. This article presents a critical comparison of existing CGM technologies, highlighting critical issues of device accuracy, foreign body response, calibration, and miniaturization. An outlook on future developments with an emphasis on long-term reliability and performance is also presented.

A low frequency electromagnetic sensor for indirect measurement of glucose concentration: in vitro experiments in different conductive solutions

In recent years there has been considerable interest in the study of glucose-induced dielectric property variations of human tissues as a possible approach for non-invasive glycaemia monitoring. We have developed an electromagnetic sensor, and we tested in vitro its ability to estimate variations in glucose concentration of different solutions with similarities to blood (sodium chloride and Ringer-lactate solutions), differing though in the lack of any cellular components. The sensor was able to detect the effect of glucose variations over a wide range of concentrations (∼78–5,000 mg/dL), with a sensitivity of ∼0.22 mV/(mg/dL). Our proposed system may thus be useful in a new approach for non-invasive and non-contact glucose monitoring.

Full-field optical coherence tomography for the rapid estimation of epidermal thickness: study of patients with diabetes mellitus type 1

Changes in morphology of the skin are an important factor that can affect non-invasive measurements performed through this organ, in particular for glucose monitoring in e.g. patients with diabetes mellitus. A characterization technique for non-contact in vivo profiling of the superficial skin layers can be beneficial for evaluation of the performance of such measurement systems. We applied a full-field optical coherence tomography (OCT) system followed by the fully automatic processing for this task. With the developed procedure, non-invasive quantification of the skin morphology can be performed within a few minutes. The dorsal skin of the upper arm of 22 patients with Type 1 Diabetes Mellitus was investigated with an OCT system and with a commercially available dermatological laser scanning confocal microscope (CM) as a reference method. The estimates of epidermal thickness from OCT were compared with the results of expert-assisted analysis of confocal images. The highest correlation with the CM measurements has been obtained for the distance from the entrance peak to the first minimum of the OCT reflection profile (R2 = 0.657, p < 0.0001). In this specific patient group, we have observed a statistically significant correlation of the subjects' body mass index with the distance from the entrance peak to the dermal reflection peak in the OCT profile (p = 0.010). Furthermore, the same OCT parameter is negatively correlated with age with marginal statistical significance (p = 0.062). At the same time, no relation of diabetes-related parameters (duration of disease and concentration of glycated haemoglobin) to the skin morphology observed with the OCT and CM was found.