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Related Concept Videos

Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
Hypoglycemia01:26

Hypoglycemia

Hypoglycemia is a blood glucose level below 70 mg/dL. It commonly occurs in individuals using insulin or insulin-secreting drugs, but may also arise in non-diabetic conditions. People with type 1 diabetes are at the highest risk because they depend on exogenous insulin. People with type 2 diabetes are also at risk, especially when treated with insulin or medications such as sulfonylureas, which increase insulin release regardless of blood glucose levels. It develops when insulin levels exceed...
Hyperglycemia01:29

Hyperglycemia

Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose levels exceed 180 mg/dL two...
Hyperosmolar Hyperglycemic State01:21

Hyperosmolar Hyperglycemic State

Hyperosmolar Hyperglycemic State, or HHS, is a serious and life-threatening complication of type 2 diabetes mellitus. It is characterized by three main features: severe hyperglycemia, profound dehydration, and elevated serum osmolality, all occurring without significant ketoacidosis.HHS typically develops in older adults or individuals with limited access to fluids. This may result from illness, cognitive impairment, or medications such as diuretics or corticosteroids. These factors reduce...
Hormones Regulating Blood Glucose01:16

Hormones Regulating Blood Glucose

Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
In addition to accelerating glucose uptake and utilization, insulin has...
Glucose Homeostasis: Regulation of Blood Glucose01:02

Glucose Homeostasis: Regulation of Blood Glucose

Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
During fasting, when blood glucose levels are low, the pancreas secretes glucagon. it...

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Hyperglycemic Clamp and Hypoglycemic Clamp in Conscious Mice
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Block based neural network for hypoglycemia detection.

Phyo Phyo San1, Sai Ho Ling, Hung T Nguyen

  • 1Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. PhyoPhyo.San@student.uts.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an evolvable block-based neural network (BBNN) for early hypoglycemia detection using electrocardiogram (ECG) data. The BBNN with hybrid particle swarm optimization achieved 76.74% sensitivity and 50.91% specificity in detecting hypoglycemic episodes.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Computational Biology

Background:

  • Hypoglycemia episodes pose significant risks, especially for Type 1 Diabetes Mellitus (T1DM) patients.
  • Early detection of hypoglycemia is crucial for timely intervention and preventing severe complications.
  • Traditional methods may struggle with the variability of electrocardiogram (ECG) signals.

Purpose of the Study:

  • To develop and evaluate an evolvable block-based neural network (BBNN) for accurate hypoglycemia episode detection.
  • To utilize physiological parameters from ECG signals, specifically heart rate (HR) and corrected QT interval (QTc), as inputs for the BBNN.
  • To optimize the BBNN structure and weights using an evolutionary algorithm for enhanced adaptability to ECG variations.

Main Methods:

  • An evolvable block-based neural network (BBNN) architecture was designed, featuring a 2D array of blocks with variable configurations.
  • Physiological parameters (HR, QTc) derived from ECG signals were used as input features for the BBNN.
  • A hybrid particle swarm optimization with wavelet mutation (HPSOWM) algorithm was employed to optimize the BBNN's structure and weights.
  • ECG data from 15 T1DM patients were divided into training, testing, and validation sets for model evaluation.

Main Results:

  • The proposed BBNN with HPSOWM demonstrated successful detection of hypoglycemic episodes in T1DM patients.
  • The model achieved a testing sensitivity of 76.74% and a testing specificity of 50.91%.
  • The optimized BBNN structure effectively compensated for individual and temporal variations in ECG patterns.

Conclusions:

  • The evolvable BBNN, optimized via HPSOWM, presents a promising approach for early hypoglycemia detection using ECG data.
  • The method's ability to adapt to ECG signal variability enhances its potential for clinical application in T1DM management.
  • Further validation on larger datasets could solidify its role in continuous glucose monitoring and hypoglycemia prevention.