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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

6.8K
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...
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ECG Interpretation of Rhythms01:24

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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...
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Pulse rhythm01:30

Pulse rhythm

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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...
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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Related Experiment Video

Updated: Feb 21, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Exercise ECG classification based on HRV features induced by robust R-peak detection model.

Xinhua Su1, Xuxuan Wang1, Huanmin Ge1

  • 1School of Sports Engineering (China Big Data Center for Sports), Beijing Sport University, Beijing, China.

Computer Methods in Biomechanics and Biomedical Engineering
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces UNet-M-D for accurate R-peak detection in noisy exercise electrocardiograms (ECGs), improving fatigue classification for sports health management.

Keywords:
ECGHRVR-peak detectiondynamic convolutionfatigue classificationtransformer

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Area of Science:

  • Sports Science
  • Biomedical Engineering
  • Cardiovascular Physiology

Background:

  • Accurate R-peak detection in exercise electrocardiograms (ECGs) is crucial for assessing exercise-induced fatigue through heart rate variability (HRV) analysis.
  • Existing models struggle with the noise inherent in ECGs recorded during physical activity.
  • Developing robust R-peak detection methods is essential for reliable fatigue assessment in sports.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model, UNet-M-D, for precise R-peak detection in noisy exercise ECG signals.
  • To assess the effectiveness of the proposed model in improving the accuracy of exercise-induced fatigue classification.
  • To provide a foundation for enhanced sports health management and training adjustments based on objective fatigue measures.

Main Methods:

  • Proposed UNet-M-D model integrating positional encoding, multi-head self-attention, and dynamic convolution for enhanced R-peak detection.
  • Model evaluation using the GUDB and EPFL ECG datasets, known for containing exercise-induced noise.
  • Feature selection from heart rate variability (HRV) metrics derived from the detected R-peaks for subsequent fatigue classification.

Main Results:

  • UNet-M-D achieved superior R-peak detection accuracy, reaching up to 99.2% on the evaluated datasets.
  • The model demonstrated significant noise resilience, performing well in signal-to-noise ratios (SNR) as low as 6-18 dB.
  • Fatigue classification accuracy reached 77.4% using optimally selected HRV features derived from the UNet-M-D R-peak detection.

Conclusions:

  • The UNet-M-D model offers a robust and accurate solution for R-peak detection in challenging exercise ECG conditions.
  • Improved R-peak detection directly translates to more reliable HRV feature extraction and subsequent fatigue classification.
  • This research provides a valuable tool for objective sports health monitoring and personalized training regimen optimization.