A Review of Continuous Blood Glucose Monitoring and Prediction of Blood Glucose Level for Diabetes Type 1 Patient in Different Prediction Horizons (PH) Using Artificial Neural Network (ANN)

2019 ◽  
pp. 684-695
Author(s):  
Muhammad Asad ◽  
Usman Qamar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Monitoring ◽  
Diabetes Type ◽  
Blood Glucose Monitoring ◽  
Diabetes Type 1 ◽  
Artificial Neural Network Ann

scholarly journals Analysis of time in range depending on the method of assessing the glucose level

2021 ◽  
pp. 46-55
Author(s):  
L. A. Suplotova ◽  
A. S. Sudnitsyna ◽  
N. V. Romanova ◽  
K. A. Sidorenko ◽  
L. U. Radionova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Level ◽  
Glucose Monitoring ◽  
Diabetes Type ◽  
Diabetes Type 1 ◽  
Self Monitoring ◽  
Patients With Diabetes ◽  
Time In Range

Introduction. In recent years, there has been an increase in the prevalence and incidence diabetes type 1. The high-quality glycemic control is critical in reducing the risk of developing and progression of vascular complications and adverse outcomes of diabetes. Self-monitoring blood glucose (SMBG) and professional continuous glucose monitoring (PCGM) provide the data set which must be interpreted using multiple indicators of glycemic control. A number of researchers have demonstrated the relationship between the time in range (TIR) and the risk of developing both micro- and macrovascular complications of diabetes. Considering the insufficient amount of data on TIR differences depending on the glucose level assessment method and the significant potential of using this indicator for the stratification of the risk of both micro- and macrovascular complications of diabetes, the study of TIR differences based on the data of PCGM and SMBG is relevant at present.Aims. To estimate the time range according to professional continuous glucose monitoring and self-monitoring of blood glucose levels in the patients with diabetes type 1 among the adult population to improve the control of the disease course.Materials and methods. An interventional open-label multicenter study in the patients with diabetes type 1 was conducted. The patients with diabetes type 1 aged 18 and older, with the disease duration of more than 1 year receiving the therapy with analog insulin was enrolled into the study. The calculation of the indicators of the time spent in the ranges of glycemia was carried out on the basis of the data of PCGM and SMBG.Results and discussion. We examined 218 patients who met the inclusion criteria and did not have exclusion criteria. The presented differences in the indicators of time in ranges indicate the comparability of the SMBG and PCGM methods.Conclusions. When assessing the indicators of time in the ranges of glycemia obtained on the basis of the data of PCGM and SMBG, clear correlations and linear dependence were demonstrated, which indicates the comparability of these parameters regardless of the measurement method.

Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network

2018 ◽  
Vol 38 (4) ◽  
pp. 828-840 ◽  
Author(s):  
Jaouher Ben Ali ◽  
Takoua Hamdi ◽  
Nader Fnaiech ◽  
Véronique Di Costanzo ◽  
Farhat Fnaiech ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Artificial Neural

136-OR: Nocturnal Hypoglycemia Prevention Strategies Used by People with Type 1 Diabetes According to Their Insulin Delivery and Blood Glucose Monitoring Technology

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 136-OR
Author(s):  
MERYEM K. TALBO ◽  
VIRGINIE MESSIER ◽  
KATHERINE DESJARDINS ◽  
RÉMI RABASA-LHORET ◽  
ANNE-SOPHIE BRAZEAU ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Monitoring ◽  
Insulin Delivery ◽  
Blood Glucose Monitoring ◽  
Prevention Strategies ◽  
Monitoring Technology ◽  
Nocturnal Hypoglycemia

scholarly journals Comparison between adherence assessments and blood glucose monitoring measures to predict A1c in type 1 diabetes

2015 ◽  
Vol 7 (S1) ◽  
Author(s):  
Gabriela Heiden Teló ◽  
Martina Schaan de Souza ◽  
Thaís Sturmer Andrade ◽  
Beatriz D'Agord Schaan
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring

Design of Automatic Insulin Dosage Indicator for Diabetic Patients Using BMI

2020 ◽  
pp. 69-74
Author(s):  
Kanimozhi R ◽  
Saravanakumar S
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Blood Glucose Concentration ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Insulin Dosage ◽  
Diabetic Patients ◽  
Monitoring Methods ◽  
Invasive Techniques

Diabetes Mellitus is a serious and chronic health disease. It occurs in all age group of people, especially in adults and aged persons. It is important to measure blood glucose level frequently for the diabetes affected persons which in need to determine the appropriate insulin dosage. Along with this, the continuous glucose monitoring is vital to know whether the glucose level is in normal range. The conventional method used to measure the glucose level in blood is invasive which is infectious and a painful process. Nowadays, the non-invasive blood glucose monitoring methods are widely used. In this work, the blood glucose level is measured non-invasively using IR sensor. Besides that, the indication of insulin dosage to be taken is done by determining blood glucose concentration (non- invasively) and comparing it with Body Mass Index (BMI) of the patient. The implementation is based on the variations in the intensity of the IR LED, BMI and blood density. Themethod ismore reliable than the invasive techniques.

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