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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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 the T...
Pulse Oximetry01:24

Pulse Oximetry

Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
Purpose
Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...
Special considerations while measuring oxygen saturation01:19

Special considerations while measuring oxygen saturation

Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
Ensuring accuracy in vital sign recordings while prioritizing patient comfort and minimizing anxiety is important. 
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
Exercise Stress Test01:26

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
Pulse rhythm01:30

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 muscle...

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Related Experiment Video

Updated: Jun 13, 2026

Experimental Protocol of a Three-minute, All-out Arm Crank Exercise Test in Spinal-cord Injured and Able-bodied Individuals
07:32

Experimental Protocol of a Three-minute, All-out Arm Crank Exercise Test in Spinal-cord Injured and Able-bodied Individuals

Published on: June 8, 2017

AI-Powered Resting 12-Lead Electrocardiogram Algorithm for Predicting Low Peak Oxygen Consumption: Development and

Shu-Chun Huang1,2,3, Tieh-Cheng Fu1,4, Michelle Liou5

  • 1School of Medicine, College of Medicine, Taoyuan, Taiwan.

JMIR Medical Informatics
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

Low peak oxygen consumption (V̇O2) is linked to higher mortality, but practical screening is lacking. An AI-powered ECG analysis effectively identifies individuals with low V̇O2 for early intervention.

Keywords:
AIartificial intelligencecardiopulmonary exercise testcardiorespiratory fitnessdeep learninggradient-boosting classifierwavelet transform

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

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Related Experiment Videos

Last Updated: Jun 13, 2026

Experimental Protocol of a Three-minute, All-out Arm Crank Exercise Test in Spinal-cord Injured and Able-bodied Individuals
07:32

Experimental Protocol of a Three-minute, All-out Arm Crank Exercise Test in Spinal-cord Injured and Able-bodied Individuals

Published on: June 8, 2017

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

Area of Science:

  • Cardiology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Low peak oxygen consumption (V̇O2) is a significant predictor of cardiovascular and all-cause mortality.
  • Improving peak V̇O2 can reduce mortality risk, necessitating early detection and intervention.
  • Current screening tools for low peak V̇O2 are limited, highlighting the need for practical alternatives.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-driven algorithm using resting 12-lead electrocardiograms (ECGs) to detect low peak V̇O2.
  • To assess the efficacy of ECG analysis, individual characteristics, and their combination in predicting low peak V̇O2.
  • To establish a non-invasive screening method for early identification of individuals at risk due to low cardiorespiratory fitness.

Main Methods:

  • A neural network was trained on resting ECG data from 965 individuals to estimate peak V̇O2.
  • The model was validated on an independent cohort of 242 individuals.
  • Wavelet analysis was employed for feature extraction from ECG signals, and different input models (individual characteristics, ECG alone, ECG plus individual characteristics) were compared.

Main Results:

  • The AI-ECG model demonstrated strong performance in predicting low peak V̇O2, with an area under the curve of 0.89 in cross-validation and 0.87 in external validation.
  • Combining ECG data with individual characteristics yielded the highest accuracy for low peak V̇O2 prediction.
  • The model achieved a precision of 0.72 and recall of 0.72 in cross-validation, and 0.67 and 0.61 in external validation, respectively.

Conclusions:

  • An AI-powered algorithm analyzing resting ECGs shows significant potential for screening low peak V̇O2.
  • This approach enables early identification of individuals with reduced cardiorespiratory fitness.
  • Timely clinical intervention can be facilitated, potentially mitigating associated mortality risks.