Academic literature on the topic '«Clark Error Grid»'

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

Telah dilakukan optimasi panjang pengupasan sensor serat optik singlemode untuk memperolehsensitivitas dan akurasi yang tinggi pada sensor pengukuran gula darah. Panjang pengupasan divariasikan antara 1 cm hingga 5 cm dengan interval 1 cm. Serat optik digunakan untuk memandu cahaya dari sumber laser dioda merah (λ=650nm) ke detektor fotodioda. Interaksi antara gelombang evanescent dan molekul glukosa dalam darah meningkat karena sebagian cladding pada serat optik dikupas. Perbedaan kedalaman penetrasi karena panjang pengupasan mempengaruhi sensitivitas dan akurasi sensor yang telah dirancang. Sensitivitas tertinggi yaitu 1,034 mV/(mg/dL) yang diperoleh pada panjang pengupasan 3 cm. Sensitivitas terendah diperoleh pada panjang pengupasan 5 cm dengan nilai 0,453 mV/(mg/dL). Akurasi tertinggi terdapat pada panjang pengupasan 2 cm dengan nilai 95,23% dan akurasi terendah pada panjang pengupasan 1 cm dengan nilai 93,35%. Clark Error Grid Analisis menunjukkan bahwa data tersebar 95% di daerah A dan 0,5% di daerah B yang mempenyai error yang kecil, artinya optimasi sensor gula darah akurat dan dapat diandalkan.Kata kunci: sensor gula darah, gelombang evanescent, panjang pengupasan, sensitivitas, akurasi.

Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).

Background Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM. Objective The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before. Methods We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data. Results The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values. Conclusions Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.

Hyperglycemia is detrimental to postoperative islet cell survival in patients undergoing total pancreatectomy with islet autotransplantation (TPIAT). This makes continuous glucose monitoring (CGM) a useful management tool. We evaluated the accuracy of the Dexcom G6 CGM in pediatric intensive care unit patients following TPIAT. Twenty-five patients who underwent TPIAT had Dexcom G6 glucose values compared to paired serum glucose values. All paired glucose samples were obtained within 5 minutes of each other during the first seven days post TPIAT. Data were evaluated using mean absolute difference (MAD), mean absolute relative difference (MARD), %20/20, %15/15 accuracy, and Clarke Error Grid analysis. Exclusions included analysis during the CGM “warm-up” period and hydroxyurea administration (known drug interference). A total of 183 time-matched samples were reviewed during postoperative days 2–7. MAD was 14.7 mg/dL and MARD was 13.4%, with values of 15.2%, 14.0%, 12.1%, 11.4%, 13.2% and 14.1% at days 2, 3, 4, 5, 6 and 7, respectively. Dexcom G6 had a %20/20 accuracy of 78%, and a %15/15 accuracy of 64%. Clarke Error Grid analysis showed that 77% of time-matched values were clinically accurate, and 100% were clinically acceptable. The Dexcom G6 CGM may be an accurate tool producing clinically acceptable values to make reliable clinical decisions in the immediate post-TPIAT period.

Blood glucose is a very important element in the human body, but if it is deficient or excessive, it can cause chronic disease that can lead to death. To prevent this, it is necessary to monitor cholesterol and blood glucose levels regularly, at this time the tool for measuring blood glucose levels is still an invasive method by taking a blood sample at the fingertip by injuring it. This study aims to develop a non-invasive blood glucose measuring device using a Photoacoustic Spectroscopy sensor in the range of values from a laser pulse source (λ = 650 nm) which can detect glucose signals in the blood. The method of developing this research is by designing a non-invasive measuring instrument and measuring blood glucose levels in male and female participants to create a linearity equation between blood glucose levels and the output voltage from the sensor, then mathematically obtained a polynominal equation to convert the voltage to values. blood glucose level. Validation of measuring instruments designed by comparing invasive measuring instruments as a reference, using the Clarke EGA to determine accuracy based on the classification of values of blood glucose and blood cholesterol levels based on reference. The results of the Clarke EGA analysis show that all measurement data is in Zone A so that the measuring instrument with a photoacoustic sensor can be accepted.

Objectif : Déterminer la fiabilité et la précision d’un prototype d’appareil non invasif de mesure de glucose dans le tissu interstitiel, le PGS (Photonic Glucose Sensor), en utilisant des clamps glycémiques multi-étagés. Méthodes : Le PGS a été évalué chez 13 sujets avec diabète de type 1. Deux PGS étaient testés par sujet, un sur chacun des triceps, pour évaluer la sensibilité, la spécificité, la reproductibilité et la précision comparativement à la technique de référence (le Beckman®). Chaque sujet était soumis à un clamp de glucose multi-étagé de 8 heures aux concentrations de 3, 5, 8 et 12 mmol/L, de 2 heures chacun. Résultats : La corrélation entre le PGS et le Beckman® était de 0,70. Pour la détection des hypoglycémies, la sensibilité était de 63,4%, la spécificité de 91,6%, la valeur prédictive positive (VPP) 71,8% et la valeur prédictive négative (VPN) 88,2%. Pour la détection de l’hyperglycémie, la sensibilité était de 64,7% et la spécificité de 92%, la VPP 70,8% et la VPN : 89,7%. La courbe ROC (Receiver Operating Characteristics) démontrait une précision de 0,86 pour l’hypoglycémie et de 0,87 pour l’hyperglycémie. La reproductibilité selon la « Clark Error Grid » était de 88% (A+B). Conclusion : La performance du PGS était comparable, sinon meilleure que les autres appareils sur le marché(Freestyle® Navigator, Medtronic Guardian® RT, Dexcom® STS-7) avec l’avantage qu’il n’y a pas d’aiguille. Il s’agit donc d’un appareil avec beaucoup de potentiel comme outil pour faciliter le monitoring au cours du traitement intensif du diabète. Mot clés : Diabète, diabète de type 1, PGS (Photonic Glucose Sensor), mesure continue de glucose, courbe ROC, « Clark Error Grid».
Objective: To determine the reliability and precision of a prototype of a non-invasive device for continuous measurement of interstitial glucose, the PGS (Photonic Glucose Sensor), using multi-level glycaemic clamp. Methods: The PGS was evaluated in 13 subjects with type 1 diabetes. Two PGS were tested with each subject, one on each triceps, to evaluate the sensitivity, specificity, reproducibility and accuracy compared to the reference technique, the glucose analyzer Beckman®. Each subject was submitted to a multi-level 8 hour glucose clamp at 3, 5, 8 and 12 mmol / L, 2 hours each. Results: The correlation between the PGS and the Beckman® was 0.70. For the detection of hypoglycaemia, the sensitivity was 63.4%, the specificity 91.6%, the positive predictive value (PPV) 71.8% and the negative predictive value (NPV) 88.2%. For the detection of hyperglycaemia, the sensitivity was 64.7% the specificity 92%, the PPV 70.8% and the NPV: 89.7%. The ROC (Receiver Operating Characteristics) curve showed an accuracy of 0.86 and 0.87 for hypoglycaemia and hyperglycaemia respectively. Reproducibility according to the Clark Error Grid was 88% in the A and B zone. Conclusion: The performance of the PGS was comparable or better than other continuous glucose monitoring devices on the market (Freestyle® Navigator, Medtronic Guardian® RT, Dexcom® STS-7) with the advantage that it has no needle. It is therefore an interesting device and hopefully, which could facilitate the monitoring in the intensive treatment of diabetes. Key words: Diabetes, type 1 diabetes, PGS (Photonic Glucose Sensor), ROC curve, Clark Error Grid, continuous glucose monitoring, CGMS.