Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

769
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
769
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.2K
Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
2.2K
Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

206
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...
206
Glucose Homeostasis: Regulation of Blood Glucose01:02

Glucose Homeostasis: Regulation of Blood Glucose

1.5K
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...
1.5K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.5K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
2.5K
Insulin: Dosing Regimen and Adverse Effects01:16

Insulin: Dosing Regimen and Adverse Effects

155
Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
The basal dose constitutes about 40%-50% of the total daily dose, with the rest as premeal insulin. The mealtime insulin dose should mirror...
155

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The roles of neuroticism, genetic susceptibility, and amygdala structure in chronic musculoskeletal pain onset and recovery.

Communications medicine·2026
Same author

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026
Same author

Genomic characterisation of the outbreak-associated hantavirus strain.

Infectious diseases (London, England)·2026
Same author

Integrated Downstream Analysis and Epidemiological Modelling of Hantavirus Infection: From Host Transcriptomics to Transmission Dynamics.

Pathogens (Basel, Switzerland)·2026
Same author

Muscular Fitness Components in Adults With Type 1 Diabetes: A Cross-Sectional Study.

Diabetes/metabolism research and reviews·2026
Same author

Integrated Evolutionary and Multi-Omic Analysis of STAT Family Activation Across Solid Tumors.

Genes·2026

Related Experiment Video

Updated: Jun 11, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

19.8K

Forecasting glucose values for patients with type 1 diabetes using heart rate data.

Raffaele Giancotti1, Pietro Bosoni2, Patrizia Vizza1

  • 1Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Italy.

Computer Methods and Programs in Biomedicine
|September 27, 2024
PubMed
Summary

This study introduces a new framework using a Gated Recurrent Unit (GRU) model to improve glucose level prediction for Type 1 Diabetes Mellitus (T1DM) patients. The model enhances Continuous Glucose Monitoring (CGM) accuracy by incorporating heart rate (HR) and interstitial glucose (IG) data, reducing hypoglycemia risks.

Keywords:
AttentionContinuous glucose monitoringDiabetesHeart rateNeural networkPrediction

More Related Videos

Simple Continuous Glucose Monitoring in Freely Moving Mice
03:25

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

5.2K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.5K

Related Experiment Videos

Last Updated: Jun 11, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

19.8K
Simple Continuous Glucose Monitoring in Freely Moving Mice
03:25

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

5.2K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.5K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Metabolic Disease Management

Background:

  • Type 1 Diabetes Mellitus (T1DM) necessitates continuous glucose monitoring and insulin management.
  • Current Continuous Glucose Monitoring (CGM) systems face limitations including time lag and prediction accuracy.
  • Accurate glucose prediction is crucial for preventing complications like hypoglycemia.

Purpose of the Study:

  • To develop and validate a novel framework for enhanced glucose level forecasting in T1DM patients.
  • To improve the precision and reliability of short- and long-term glucose predictions.
  • To optimize CGM systems by integrating physiological data for better diabetes management.

Main Methods:

  • A Gated Recurrent Unit (GRU) model was employed to forecast glucose values.
  • The framework utilizes heart rate (HR) and interstitial glucose (IG) data for predictions.
  • Trained and validated on the OhioT1DM Dataset and two additional T1DM datasets.

Main Results:

  • The GRU-based framework demonstrated superior accuracy in forecasting interstitial glucose (IG) values.
  • Achieved improved Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) across various prediction horizons.
  • The open-source framework is available for further research and development.

Conclusions:

  • The proposed framework offers a significant advancement in glucose level prediction for T1DM management.
  • Integration of HR and IG data with GRU models enhances prediction accuracy and mitigates hypoglycemia risks.
  • This approach holds promise for optimizing automated insulin delivery systems and patient outcomes.