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

Electrocardiogram01:29

Electrocardiogram

7.6K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
7.6K
Exercise Stress Test01:26

Exercise Stress Test

1.9K
Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes
1.9K

You might also read

Related Articles

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

Sort by
Same author

Design and evaluation of injectable niclosamide nanocrystals prepared by wet media milling technique.

Drug development and industrial pharmacy·2014
Same author

Downregulation of leaf flavin content induces early flowering and photoperiod gene expression in Arabidopsis.

BMC plant biology·2014
Same author

Fluoride affects calcium homeostasis and osteogenic transcription factor expressions through L-type calcium channels in osteoblast cell line.

Biological trace element research·2014
Same author

Malignant melanoma of the vagina: A case report and review of the literature.

Oncology letters·2014
Same author

Synthesis and reactivity of new aminophenolate complexes of nickel.

Molecules (Basel, Switzerland)·2014
Same author

Electrochemical immunosensor for α-fetoprotein detection using ferroferric oxide and horseradish peroxidase as signal amplification labels.

Analytical biochemistry·2014

Related Experiment Video

Updated: Mar 27, 2026

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
06:00

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

Published on: July 27, 2015

13.2K

Fatigue Identification Models for Firefighters Based on Electrocardiogram Signals.

Xin Zheng1, Rui Hao, Huan Wang

  • 1School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.

Journal of Occupational and Environmental Medicine
|March 25, 2026
PubMed
Summary

Firefighter physical fatigue can be objectively identified using electrocardiogram (ECG) data. Machine learning models accurately detect fatigue, enhancing firefighter safety and occupational monitoring.

Keywords:
ElectrocardiogramFatigue recognitionFirefightersOccupational healthPhysical fatigue

More Related Videos

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.0K
Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test
10:21

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test

Published on: September 22, 2023

1.1K

Related Experiment Videos

Last Updated: Mar 27, 2026

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
06:00

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

Published on: July 27, 2015

13.2K
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.0K
Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test
10:21

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test

Published on: September 22, 2023

1.1K

Area of Science:

  • Physiological monitoring
  • Occupational health
  • Biomedical engineering

Background:

  • Firefighting demands intense physical exertion, leading to training-induced fatigue.
  • Accurate fatigue assessment is crucial for firefighter safety and performance.
  • Current methods for fatigue monitoring in firefighters have limitations.

Purpose of the Study:

  • To identify physiological indicators of physical fatigue in firefighters.
  • To develop effective machine learning models for fatigue recognition.
  • To support occupational fatigue monitoring and safety management.

Main Methods:

  • Analyzed heart rate variability (HRV) using electrocardiogram (ECG) signals from 18 firefighters before and after physical training.
  • Processed ECG data with linear and nonlinear methods, reducing feature dimensionality.
  • Developed and evaluated fatigue recognition models using machine learning and deep learning algorithms.

Main Results:

  • Physical fatigue increased mean heart rate and the LF/HF ratio, while decreasing HRV indicators like mean R-R interval and sample entropy.
  • A least absolute shrinkage and selection operator (LASSO) based one-dimensional convolutional neural network achieved 94.64% classification accuracy.
  • Significant decreases were observed in eight HRV-derived indicators due to fatigue.

Conclusions:

  • ECG-derived indicators offer a feasible and objective method for identifying firefighter physical fatigue.
  • This approach can enhance occupational fatigue monitoring and improve safety management.
  • Physiological monitoring through ECG analysis is a promising tool for firefighter well-being.