Clarke's Grid Research Articles

Non-invasive glucometer monitoring system through optical based near-infrared sensor method

ABSTRACT Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

Comparison between mathematical methods to estimate blood glucose levels from ECG signals

Diabetes mellitus, known as diabetes, is a chronic disease that affects the control of blood glucose concentration levels, it is a disease that mostly affects adults (type 2 diabetes), but it can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the diseases with the highest prevalence and high mortality worldwide. Diabetes has no cure, but continuous monitoring to maintain blood glucose levels in normal ranges reduces the possibility of suffering from gastrointestinal problems, vision loss, limb amputations (such as diabetic foot) and damage to vital organs such as the heart and kidneys, among other associated complications. This article compares the results in glucose estimation by using a linear, quadratic and cubic regression considering the electrical characteristics generated in the cardiac conduction (HR, HRV, T-wave peak, and QT interval) recorded on a single-lead electrocardiogram (VII), used as a non-invasive blood glucose estimation model. The best estimate was obtained using a cubic regression. The validation was performed using the Clarke grid having 77.78 % of data in the A zone and 22.22 % in the B zone and a Pearson correlation value of 0.94103 in the cubic regression.

Numerical and clinical precision in hypoglycemia of the intermittent FreeStyle Libre glucose monitoring through an NFC-Bluetooth transmitter associated with the xDrip+ algorithm in diabetic patients under insulin therapy

IntroductionThere are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established. MethodsStudy of diagnostic tests. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+ app, in diabetic patients managed with insulin therapy. ResultsTwenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+ app (81.92% vs. 68.67%, p=0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip+ and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p=0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p=0.0273), being higher with FreeStyle libre. ConclusionsThe use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+ app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.

Precisión numérica y clínica en hipoglucemia de la monitorización intermitente de glucosa FreeStyle Libre a través de un transmisor NFC-Bluetooth asociado al algoritmo xDrip+ en pacientes diabéticos en insulinoterapia

IntroductionThere are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established. MethodsStudy of diagnostic tests. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+app, in diabetic patients managed with insulin therapy. ResultsTwenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+app (81.92% vs. 68.67%, p=0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip+and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p=0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p=0.0273), being higher with FreeStyle libre. ConclusionsThe use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.

An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi

Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.

Commissioning procedure for predictive control based on ARX models of Type 1 Diabetes Mellitus patients

This work presents a systematic way to assist on the commissioning of the Predictive Functional Control (PFC) algorithm to be implemented in the context of the Artificial Pancreas (AP) for patients with Type 1 Diabetes Mellitus (T1DM). PFC has demonstrated its high potentiality in process industry since several decades ago. Its use for AP has been specifically adapted and tested in the well validated model of the endocrine system, known as the Uva/Padova model. Firstly, the tests were done in silico using the parameters of 30 patients: 10 children, 10 adolescents and 10 adults. The performance of the controller was evaluated by means of the Control Variability Grid Analysis (CVGA) giving excellent results. We propose here the use of data driven models, as a second step, to perform a PFC final tuning to be practically ready to use in AP implementations. The closed loop responses are tested through dynamic simulations with the obtained models when different meals intake and insulin dosages are given to the patient Nº 5004 from the Center of Diabetes Technology (UVa/USA). In this paper an ARX model is build based on the patient’s own historical data recorded during 27 days. The ARX model was used giving good results at 30 min. prediction such as 89.1% of FIT, RMSE=10.312 mg/dl, and the Clarke Grid 97.58% at Zone A and 2.42% at B, while for 60 min. prediction, the FIT was 65.5%, RMSE 25.945 mg/dl and the Clarke Grid 88.93% at Zone A and 11.07% at B. The simplified models used in the PFC structure were obtained from the ARX model to isolate the insulin impact from the carbohydrates effects on the blood glucose variations. Finally, the full closed loop simulations, taking into account the constraints related to the insulin pump, were performed to conclude the PFC design. Closing the work, comparisons between the blood glucose behavior without control and with PFC and PID implementations are given to drive the final conclusions and discuss future works.

Limitations of Statistical Measures of Error in Assessing the Accuracy of Continuous Glucose Sensors

Various statistical methods are commonly used to assess the accuracy of near-continuous glucose sensors. The performance and reliability of these methods have not been well described. We used computer simulation to describe the behavior of several statistical measures including error grid analysis, receiver operating characteristics, correlation, and repeated measures under varying conditions. Actual data from an inpatient accuracy study conducted by the Diabetes Research in Children Network (DirecNet) were also used to demonstrate these limitations. Sensors that were made artificially inaccurate by randomly shuffling the pairings to reference values still fell in Zone A or B 78% of the time for the Clarke grid and 79% of the time for the modified grid. Area under the curve values for these shuffled pairs averaged 64% for hypoglycemia and 68% for hyperglycemia. Continuous error grid analysis resulted in 75% of shuffled pairs designated as "Accurate Readings" or "Benign Errors." Correlation analysis gave inconsistent results for sensors simulated to have identical accuracies with values ranging from 0.50 to 0.96. Simplistic repeated-measures analyses accounting for subject effects, but ignoring temporal correlation patterns substantially inflated the probability of falsely obtaining a statistically significant result. In simulations where the null hypothesis was correct, 23% of observed P values were <0.05 and 12% of observed P values were <0.01. Commonly used statistical methods can give overly optimistic and/or inconsistent notions of sensor accuracy if results are not placed in proper context. Novel techniques are needed to assess the accuracy of near-continuous glucose sensors.