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Related Concept Videos

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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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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Electrocardiogram01:29

Electrocardiogram

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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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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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Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
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Related Experiment Video

Updated: Jan 9, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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P-Wave Spatial Variability in 12-Lead ECG Predicts Temporal Changes in Atrial Fibrillation.

Dimitris Filos, Maria Toumpourleka, Dimitris Tachmatzidis

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    Machine learning models can predict atrial fibrillation (AF) patient categories using P-wave variability from standard ECGs. This approach aids in early AF risk stratification during sinus rhythm, improving resource allocation.

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    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Atrial fibrillation (AF) is linked to atrial remodeling.
    • P-wave indices may predict AF risk.
    • AF patients show greater P-wave morphology variability.

    Purpose of the Study:

    • Categorize AF patients using P-wave temporal variability.
    • Classify AF patients via P-wave characteristics from standard ECGs.
    • Investigate machine learning for AF stratification.

    Main Methods:

    • Unsupervised learning (k-means) for patient categorization.
    • Feature extraction from 12-lead ECG P-waves.
    • Supervised learning (SVM, Random Forest) for classification.
    • Boruta algorithm and voting for feature selection.

    Main Results:

    • Two AF patient categories identified via VCG P-wave variability.
    • Random Forest model achieved 80.2% accuracy.
    • Temporal P-wave variation predictable from spatial ECG characteristics.

    Conclusions:

    • Machine learning can stratify AF patients using standard ECGs.
    • This method offers a cost-effective approach for early risk identification.
    • Larger datasets are needed for enhanced model validation.