Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pulse rhythm

Pulse rhythm

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...

You might also read

Related Articles

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

Sort by

Same author

Type 1 diabetes stigma: A UK survey of adults living with type 1 diabetes.

Diabetic medicine : a journal of the British Diabetic Association·2026

Same author

Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.

Journal of diabetes science and technology·2026

Same author

The Association Between Ambient Temperature and Hypoglycemia in People Living With Type 1 Diabetes: A Case Time Series Analysis Using Real-Time Continuous Glucose Monitoring.

Diabetes care·2026

Same author

Exploring inequities in access to diabetes technologies among children and young people with type 1 diabetes: Perspectives of parents and young people from ethnic minority groups and low socio-economic areas.

Diabetic medicine : a journal of the British Diabetic Association·2026

Same author

Glucose Predictions Improve Glycemic Control: A Digital Twin Evaluation.

Diabetes technology & therapeutics·2026

Same author

Characterising the impact of shift work on diet and glucose variability in healthcare employees living with type 2 diabetes: The Shift-Diabetes study.

Diabetic medicine : a journal of the British Diabetic Association·2026

Same journal

Continuous Glucose Monitoring Metrics and Perinatal Outcomes in Pregnant Women with Type 2 Diabetes.

Diabetes technology & therapeutics·2026

Same journal

Invited Commentary: Using Large Clinical Databases.

Diabetes technology & therapeutics·2026

Same journal

Impact of Technology Introduction on Glycemic Control of People Living with Type 1 Diabetes in Belgium: An Observational, Real-World Study.

Diabetes technology & therapeutics·2026

Same journal

Differences in Achieving Stringent Glycemic Targets Among Youth with Type 1 Diabetes: A SWEET Registry Study.

Diabetes technology & therapeutics·2026

Same journal

<i>Letter:</i> "Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting".

Diabetes technology & therapeutics·2026

Same journal

Transient Benign Ketosis Is Common in Type 1 Diabetes During Very-Low Carbohydrate States: Novel Data from Continuous Ketone Sensor.

Diabetes technology & therapeutics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 5, 2025

Simple Continuous Glucose Monitoring in Freely Moving Mice

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

Identifying Continuous Glucose Monitoring Data Using Machine Learning.

Pau Herrero¹, Monika Reddy², Pantelis Georgiou¹

¹Department of Electrical and Electronic Engineering, Centre for Bio-Inspired Technology, Imperial College London, London, United Kingdom.

Diabetes Technology & Therapeutics

|January 31, 2022

Summary

This summary is machine-generated.

Machine learning can identify individual continuous glucose monitoring (CGM) data, creating a digital "fingerprint." This cybersecurity approach aids in detecting glycemic changes for personalized diabetes care.

Keywords:

Continuous glucose monitoring Cybersecurity Data privacy Machine learning Type 1 diabetes

More Related Videos

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Published on: January 4, 2018

Related Experiment Videos

Last Updated: Oct 5, 2025

Simple Continuous Glucose Monitoring in Freely Moving Mice

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Published on: January 4, 2018

Area of Science:

Biomedical Informatics
Machine Learning Applications
Diabetes Technology

Background:

Wearable continuous glucose monitoring (CGM) devices generate large datasets, posing cybersecurity challenges.
Identifying individual CGM data is crucial for data security and personalized health insights.

Purpose of the Study:

To demonstrate the feasibility of identifying individual continuous glucose monitoring (CGM) data using machine learning.
To develop a method for creating a unique digital "fingerprint" for CGM data streams.

Main Methods:

Utilized the REPLACE-BG dataset (NCT02258373) with 226 adult type 1 diabetes participants using CGM.
Trained a support vector machine (SVM) binary classifier on 12 glycemic metrics across various time periods and data window lengths (3-30 days).
Employed recursive feature selection to identify the minimal feature subset for accurate classification.

Main Results:

A 15-day data window achieved the highest accuracy (86.8%) and F1 score (0.86).
Sensitivity and specificity were 85.7% and 87.9%, respectively.
Recursive feature selection reduced the feature set to 9, maintaining similar performance.

Conclusions:

Machine learning techniques can accurately identify individual CGM data streams.
The developed approach can serve as a digital CGM "fingerprint" for enhanced data security.
This method can also detect glycemic fluctuations within individuals, aiding in managing conditions like illness.