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Related Concept Videos

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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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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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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Related Experiment Video

Updated: Dec 23, 2025

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic

Sarah W E Baalman1, Florian E Schroevers2, Abel J Oakley2

  • 1Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Meibergdreef 9, Amsterdam, the Netherlands.

International Journal of Cardiology
|April 22, 2020
PubMed
Summary

A new deep learning model accurately detects atrial fibrillation (AF) from electrocardiograms (ECGs) by focusing on the QRS complex. This approach enhances diagnostic accuracy and provides insights into the model's decision-making process.

Keywords:
Atrial fibrillationBlack boxDeep learningElectrocardiogramMorphologyVisualization

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Deep learning (DL) models show promise for atrial fibrillation (AF) detection but lack transparency.
  • The
  • black box
  • nature of DL hinders clinical trust and understanding.

Purpose of the Study:

  • Develop a morphology-based DL model to differentiate AF from sinus rhythm (SR).
  • Visualize ECG segments critical for accurate AF classification by the DL model.

Main Methods:

  • Processed 1469 ECGs from patients with AF history.
  • Normalized single cardiac cycles (SC) from ECG lead II.
  • Trained DL models using R-wave centralization and R-to-R wave scaling.
  • Employed DL-based heat mapping for visualization.

Main Results:

  • A feedforward neural network achieved 0.96 accuracy and 0.94 F1-score.
  • The QRS complex onset was identified as the most discriminative ECG feature.
  • Visualization highlighted the model's focus on specific ECG morphologies.

Conclusions:

  • The morphology-based DL model effectively discriminates AF from SR with high accuracy.
  • DL model visualization offers clinicians insights into ECG features sensitive for AF detection.