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

Exercise Stress Test

Exercise Stress Test

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

You might also read

Related Articles

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

Sort by

Same author

Artificial Intelligence Is Not a Useful Tool in Exercise Science and Sports Medicine.

Medicine and science in sports and exercise·2026

Same author

Artificial Intelligence Is Not a Useful Tool in Exercise Science and Sports Medicine: Response to Nindl and Freidl.

Medicine and science in sports and exercise·2026

Same author

RYR1<sup>+</sup> skeletal muscle-derived extracellular vesicles are exercise responsive and associated with insulin action.

Journal of translational medicine·2026

Same author

Immunoglobulin G and plasma glycome in early rheumatoid arthritis differ from matched controls and link less fat-free mass to an accelerated aging phenotype: an exploratory study.

Arthritis research & therapy·2026

Same author

From Intention to Maintenance: A Two-Year Examination of Clinical and Lifestyle Changes in Rheumatoid Arthritis.

ACR open rheumatology·2025

Same author

Entropy as a marker of physiological transition during pediatric cardiopulmonary exercise testing.

Frontiers in physiology·2025

Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026

Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026

Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026

Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026

Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 28, 2025

Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise

Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise

Published on: December 19, 2024

Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise

Julio J Portella, Brian J Andonian, Donald E Brown

IEEE Journal of Biomedical and Health Informatics

|March 30, 2022

Summary

This summary is machine-generated.

Machine learning simplifies Cardiopulmonary Exercise Testing (CPET) interpretation. This tool enhances accessibility for clinicians by providing clear visualizations and scores for cardiac, pulmonary, and other exercise limitations.

More Related Videos

Integration of Brain Tissue Saturation Monitoring in Cardiopulmonary Exercise Testing in Patients with Heart Failure

Integration of Brain Tissue Saturation Monitoring in Cardiopulmonary Exercise Testing in Patients with Heart Failure

Published on: October 1, 2019

Author Spotlight: Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

Author Spotlight: Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

Published on: May 12, 2023

Related Experiment Videos

Last Updated: Sep 28, 2025

Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise

Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise

Published on: December 19, 2024

Integration of Brain Tissue Saturation Monitoring in Cardiopulmonary Exercise Testing in Patients with Heart Failure

Integration of Brain Tissue Saturation Monitoring in Cardiopulmonary Exercise Testing in Patients with Heart Failure

Published on: October 1, 2019

Author Spotlight: Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

Author Spotlight: Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

Published on: May 12, 2023

Area of Science:

Physiology
Medical Informatics
Machine Learning

Background:

Cardiopulmonary Exercise Testing (CPET) is crucial for assessing exercise response and identifying limitations.
Interpreting complex CPET data requires specialized training and significant clinical experience.
Current data visualization methods for CPET are challenging for widespread clinical adoption.

Purpose of the Study:

To develop a simplified data interpretation and visualization tool for CPET using machine learning.
To enhance the accessibility of CPET results for clinicians.
To augment existing diagnostic procedures with an AI-driven platform.

Main Methods:

Investigated machine learning algorithms for CPET data analysis.
Developed a visualization tool displaying cardiac, pulmonary, and other limitations.
Utilized three independent random forest classifiers to define limitation values.
Created an interactive dashboard with scores and interpretability plots for clinician understanding.

Main Results:

Successfully developed a machine learning platform for CPET data interpretation.
The visualization tool categorizes limitations into cardiac, pulmonary, and other.
An interactive dashboard provides interpretable scores and plots for clinicians.
The platform demonstrates potential for simplifying complex CPET data.

Conclusions:

The developed machine learning tool can make CPET more accessible to clinicians.
This platform has the potential to augment current diagnostic procedures.
Simplified interpretation and visualization can improve the clinical utility of CPET.