scholarly journals Predictive Glucose Monitoring for People with Diabetes Using Wearable Sensors

2021 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Dawn Adams ◽  
Ejay Nsugbe
Keyword(s):  
Blood Glucose ◽  
Glucose Level ◽  
Communicable Disease ◽  
Wearable Sensors ◽  
Glucose Monitoring ◽  
Support Vector ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Diabetic Individual ◽  
Glucose Prediction

Diabetes is a chronic non-communicable disease resulting from pancreatic inability to produce the hormone insulin, or a physiological cellular inability to use this hormone effectively. This leads to unregulated blood glucose levels, which can cause significant and often irreversible physiological damage. Current means of glucose level monitoring range from infrequent capillary blood glucose sampling to continuous interstitial fluid glucose monitoring. However, the accuracy of these methods is limited by numerous factors. A potential solution to this shortcoming involves the use of wearable sensors that record an individual’s physiological responses to a range of daily activities, which are subsequently fused and processed with machine learning (ML) algorithms to provide a prediction of an individual’s glucose level and can provide an artificial intelligence-driven glucose monitoring platform. In this paper, we conduct a comparison case study using quadratic discriminant analysis (QDA) and support vector machine (SVM) algorithms for the classification of glucose levels with data acquired from the wearable sensors of a type 1 diabetic individual. Preliminary results demonstrate predicted glucose levels with >70% accuracy, indicating potential for this approach to be used in the design of an ergonomic glucose prediction platform utilizing wearable sensors. Further work will involve the exploration of additional datasets from affordable wearables to enhance and improve the prediction power of the ML algorithms.

scholarly journals A Simple Medical Record System of Non-Invasive Blood Glucose Level Measurement Results for Diabetes Care Using Graphical User Interface (GUI) MATLAB

2021 ◽  
Vol 3 (3) ◽  
pp. 99-107
Author(s):  
Nur Hasanah Ahniar
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Medical Record ◽  
Glucose Level ◽  
Glucose Monitoring ◽  
Record System ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Measurement Results

We present a medical records system and reminders to patients of the measurement results of non-invasive blood glucose levels. Measuring blood glucose levels is vital in avoiding potential adverse health effects like diabetes. Diabetes is a chronic metabolic disorder caused by a decrease in the pancreas to produce insulin. Generally, measuring blood glucose levels using the conventional method is injure the patient's finger. Currently, the non-invasive method was famous as one of the detections of blood glucose by applying the physical properties of laser absorption. In this paper, we use the photodiode as a detector, the LED as a sensor, and a signal conditioning circuit. The results showed that non-invasive glucose monitoring has the potential to measure glucose levels with sensitivity and linearity of 3.21 mg/dL and 98%, respectively. As a result of measuring the blood glucose levels of the subject was displayed on the LCD module was designed. We designed a simple application and medical record using Blynk applications and GUI MATLAB for recording the measurement results of blood glucose level. In the future, applications that have been developed can be used by doctors for monitoring the measurement of the blood glucose level and provide information to patients by mobile applications, sending an email or message the measurement results, the decision of a disease or not, and reminds the re-measurement time.

The Research Least Squares Based on Ar Model of Glucose Prediction

2014 ◽  
Vol 971-973 ◽  
pp. 284-287 ◽  
Author(s):  
Yan Nian Wang ◽  
Bang An
Keyword(s):  
Time Series ◽  
Blood Glucose ◽  
Least Squares ◽  
Blood Sugar ◽  
Monitoring System ◽  
Glucose Monitoring ◽  
Time Series Modeling ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Glucose Prediction

Diabetes Clinical Research important task is to monitor and prevent the occurrence of high / low blood sugar events. Glucose monitoring system (Continous Glucose Monitoring System, CGMS) is used clinically in recent years gradually new blood glucose monitoring system, by measuring the concentration of glucose in interstitial fluid glucose fluctuations throughout the day to indirectly reflect the whole picture. It can be 30 minutes early to predict blood glucose levels as well as low blood sugar warning. Based on time-series modeling techniques, with blood glucose levels for non-stationary paper with improved self-regression model (AR) on blood glucose prediction. And the order of the model parameters were determined by the least squares method and adaptive AIC criteria. The results show that the algorithm can be based on time series modeling real-time and accurate display changes in blood glucose levels, while forecasting and early warning of low blood sugar glucose results showed good performance.

scholarly journals Association between blood glucose levels and Glasgow Outcome Score in patients with traumatic brain injury: secondary analysis of a randomized trial

Trials ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Tao Yuan ◽  
Hongyu He ◽  
Yuepeng Liu ◽  
Jianwei Wang ◽  
Xin Kang ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Blood Glucose ◽  
Brain Injury ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Threshold Effect ◽  
Glasgow Outcome Score ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Average Blood Glucose

Abstract Background Blood glucose levels that are too high or too low after traumatic brain injury (TBI) negatively affect patient prognosis. This study aimed to demonstrate the relationship between blood glucose levels and the Glasgow Outcome Score (GOS) in TBI patients. Methods This study was based on a randomized, dual-center, open-label clinical trial. A total of 208 patients who participated in the randomized controlled trial were followed up for 5 years. Information on the disease, laboratory examination, insulin therapy, and surgery for patients with TBI was collected as candidate variables according to clinical importance. Additionally, data on 5-year and 6-month GOS were collected as primary and secondary outcomes, respectively. For multivariate analysis, a generalized additive model (GAM) was used to investigate relationships between blood glucose levels and GOS. The results are presented as odds ratios (ORs) with 95% confidence intervals (95% CIs). We further applied a two- piecewise linear regression model to examine the threshold effect of blood glucose level and GOS. Results A total of 182 patients were included in the final analysis. Multivariate GAM analysis revealed that a bell-shaped relationship existed between average blood glucose level and 5-year GOS score or 6-month GOS score. The inflection points of the average blood glucose level were 8.81 (95% CI: 7.43–9.48) mmol/L considering 5-year GOS as the outcome and were 8.88 (95% CI 7.43−9.74) mmol/L considering 6-month GOS score as the outcome. The same analysis revealed that there was also a bell relationship between average blood glucose levels and the favorable outcome group (GOS score ≥ 4) at 5 years or 6 months. Conclusion In a population of patients with traumatic brain injury, blood glucose levels were associated with the GOS. There was also a threshold effect between blood glucose levels and the GOS. A blood glucose level that is either too high or too low conveys a poor prognosis. Trial registration ClinicalTrials.gov NCT02161055. Registered on 11 June 2014.

scholarly journals POLYCYSTIC OVARIAN SYNDROME;

2012 ◽  
Vol 19 (06) ◽  
pp. 786-788
Author(s):  
KIRAN BUTT ◽  
FARAH DEEBA ◽  
HAVAIDA ATTIQUE
Keyword(s):  
Blood Glucose ◽  
Lipid Profile ◽  
Glucose Level ◽  
Fasting Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Increased Risk ◽  
Commercial Kits ◽  
The University ◽  
Ovarian Syndrome

Objective: The objective of the present study was to determine the changes in the glucose level and lipid profile in patients withpolycystic ovarian syndrome (PCOS). Study Design: Descriptive study. Place and Duration of the study: This study was conducted atInstitute of Molecular Biology and Biotechnology, The University of Lahore from June 2009 to June 2010. Patients and Methods: Total 50patients with PCOS were included and 50 age-matched control subjects were also selected for comparison. Their glucose levels and lipidprofile were assessed using commercial kits. The data thus obtained was subjected to statistical analysis. Results: Significant differences(P<0.05) in fasting blood glucose level and individual parameters of lipid profile were observed in women with PCOS. A higher prevalence ofhypertriglyceridemia, hypercholesterolemia, higher LDL, lower HDL and higher fasting blood glucose levels was explored in PCOS womenthan controls. Conclusions: Abnormal glucose level and lipid profile in PCOS women showed that these women are at an increased risk ofdeveloping diabetes and subsequently cardiovascular diseases.

scholarly journals EFFECTIVENESS OF AMLA JUICE IN REDUCING BLOOD GLUCOSE LEVEL AMONG PATIENTS WITH TYPE II DIABETES IN A SELECTED AREA OF KANYAKUMARI DISTRICT

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mrs. Vanitha. S s ◽  
Dr. Pramjit kaur
Keyword(s):  
Blood Glucose ◽  
Standard Deviation ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Type Ii Diabetes ◽  
Control Group ◽  
Type Ii ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Experimental Group

Challenges in lifestyle, such as increasesin energy intake and decreasesin physical activity are causing overweight and obesity leading to epidemic increases in type II Diabetes Mellitus. The research approach used for this study was evaluative approach and the research design was true experimental design. 60 patients with type II diabetes, 30 in experimental group and 30 in control group were selected for this study by using purposive sampling technique. Data was collected with the help of self-structured interview schedule. Descriptive statistics (frequency, percentage, mean and standard deviation) and inferential statistics (chi-square, paired ‘t’ test) were used to analyse the data and to test the hypotheses. In the experimental group,the pre-test mean score was 2.966, mean percentage was 59% and standard deviation was 1.129 and in post-testmean score was 2.533, mean percentage was 50.66% and standard deviation was 1.074 with effectiveness of 8.34% and paired‘t’ test value of t=3.971,which was statistically significant (p<0.05) which is an evidence ofthe effectiveness of Amla juice in reducing blood glucose level. Comparison of blood glucose levels in experimental and control groups, shows that the value is statistically highly significant, as was observed from the unpaired ‘t’ test value of 13.39 with P value of <0.05, which is an evidence indicatingthe effect of Amla juice in reducing postprandial blood glucose levels. The resultsfound that the administration of Amla juice did have aneffect in reducing blood glucose level in the experimental group. By comparing the findings of pre-test and post test between the experimental group and the control group,the effect was identified (assessed). The study concluded that the Amlajuice is effective in reducing blood glucose level.

scholarly journals Comparison of Functional Regression and Functional Principal Component Regression for Estimating Non-Invasive Blood Glucose Level

2021 ◽  
Vol 5 (1) ◽  
pp. 14-25
Author(s):  
Nurul Fadhilah ◽  
Erfiani Erfiani ◽  
Indahwati Indahwati
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Principal Component Regression ◽  
Principal Component ◽  
Functional Regression ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Functional Principal Component

The calibration method is an alternative method that can be used to analyze the relationship between invasive and non-invasive blood glucose levels. Calibration modeling generally has a large dimension and contains multicolinearities because usually in functional data the number of independent variables (p) is greater than the number of observations (p>n). Both problems can be overcome using Functional Regression (FR) and Functional Principal Component Regression (FPCR). FPCR is based on Principal Component Analysis (PCA). In FPCR, the data is transformed using a polynomial basis before data reduction. This research tried to model the equations of spectral calibration of voltage value excreted by non-invasive blood glucose level monitoring devices to predict blood glucose using FR and FPCR. This study aimed to determine the best calibration model for measuring non-invasive blood glucose levels with the FR and FPCR. The results of this research showed that the FR model had a bigger coefficient determination (R2) value and lower Root Mean Square Error (RMSE) and Root Mean Square Error Prediction (RMSEP) value than the FPCR model, which was 12.9%, 5.417, and 5.727 respectively. Overall, the calibration modeling with the FR model is the best model for estimate blood glucose level compared to the FPCR model.

scholarly journals A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6820
Author(s):  
Bushra Alsunaidi ◽  
Murad Althobaiti ◽  
Mahbubunnabi Tamal ◽  
Waleed Albaker ◽  
Ibraheem Al-Naib
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Daily Basis ◽  
Glucose Sensing ◽  
Blood Glucose Monitoring ◽  
Optical Systems ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Modern technologies in diabetes management - continuous glucose monitoring and insulin pump therapy

2021 ◽  
Vol 87 (1) ◽  
pp. 50-55
Author(s):  
E.Yu. Pyankova ◽  
◽  
L.A. Anshakova ◽  
I.A. Pyankov ◽  
S.V. Yegorova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Insulin Pump ◽  
Glucose Monitoring ◽  
Insulin Pump Therapy ◽  
Pump Therapy ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
The Individual ◽  
Glucose Lowering Therapy

The problems of complications of diabetes mellitus cannot be solved without constant monitoring of blood glucose levels. The evolution of additional technologies for the determination of glucose in the blood of the last decades makes it possible to more accurately predict the risks of complications, both in the individual and in the patient population as a whole. The article provides an overview of the methods used in modern diabetology, facilitating control over the variability of blood glucose levels and helping in a more accurate selection of glucose-lowering therapy. All presented methods are currently working in real clinical practice in the Khabarovsk Krai

Online Prediction of Blood Glucose Levels using Genetic Algorithm

2014 ◽  
pp. 299-310
Author(s):  
Khaled Eskaf ◽  
Tim Ritchings ◽  
Osama Bedawy
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Simulation Software ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Learning Techniques ◽  
Preliminary Study ◽  
Online Prediction

Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years: from 115 million in 2000 to 284 million in 2030. This chapter is concerned with helping diabetic patients to manage themselves by developing a computer system that predicts their Blood Glucose Level (BGL) after 30 minutes on the basis of their current levels, so that they can administer insulin. This will enable the diabetic patient to continue living a normal daily life, as much as is possible. The prediction of BGLs based on the current levels BGLs become feasible through the advent of Continuous Glucose Monitoring (CGM) systems, which are able to sample patients' BGLs, typically 5 minutes, and computer systems that can process and analyse these samples. The approach taken in this chapter uses machine-learning techniques, specifically Genetic Algorithms (GA), to learn BGL patterns over an hour and the resulting value 30 minutes later, without questioning the patients about their food intake and activities. The GAs were invested using the raw BGLs as input and metadata derived from a Diabetic Dynamic Model of BGLs supplemented by the changes in patients' BGLs over the previous hour. The results obtained in a preliminary study including 4 virtual patients taken from the AIDA diabetes simulation software and 3 volunteers using the DexCom SEVEN system, show that the metadata approach gives more accurate predictions. Online learning, whereby new BGL patterns were incorporated into the prediction system as they were encountered, improved the results further.

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