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Related Concept Videos

Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

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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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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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Heart Failure IV: Classification and Diagnostic Evaluation01:30

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Disturbances in Heart Rhythm01:29

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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.
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Related Experiment Video

Updated: Nov 13, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia.

Jianwei Zheng1, Guohua Fu2, Islam Abudayyeh3

  • 1Computational and Data Science, Chapman University, Orange, CA, United States.

Frontiers in Physiology
|March 15, 2021
PubMed
Summary

A new machine learning algorithm accurately predicts ventricular tachycardia (VT) and premature ventricular complex (PVC) origins from the right or left ventricular outflow tract (RVOT/LVOT) using 12-lead ECGs.

Keywords:
artificial intelligence algorithmcatheter ablationclassificationelectrocardiographyoutflow tract ventricular tachycardia

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Machine Learning

Background:

  • Existing algorithms for identifying ventricular tachycardia (VT) and premature ventricular complex (PVC) origins from the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) using 12-lead ECGs have limitations.
  • A clinical-grade machine learning algorithm for automated analysis and prediction of VT/PVC origins is currently unavailable.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for precise prediction of RVOT and LVOT origins of VT and PVC using 12-lead ECG data.
  • To establish clinical-grade diagnostic capabilities for identifying VT/PVC ablation sites.

Main Methods:

  • A machine learning algorithm was trained using 1,600,800 features extracted from 12-lead ECGs of 420 patients who underwent successful catheter ablation (CA).
  • Data sets were randomly sampled into training (81%), validation (9%), and testing (10%) cohorts.
  • Performance was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) for optimal threshold selection.

Main Results:

  • The algorithm achieved high performance metrics: accuracy (ACC) of 97.62%, weighted F1-score of 98.46%, AUC of 98.99%, sensitivity (SE) of 96.97%, and specificity (SP) of 100%.
  • Confidence intervals for these metrics indicate robust performance across the tested data.

Conclusions:

  • The developed multistage diagnostic scheme demonstrates clinical-grade precision in predicting LVOT and RVOT origins of VT.
  • This approach offers improved applicability compared to previous studies for identifying VT/PVC ablation targets.