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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

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...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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...
Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

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 minute.
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...
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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...
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.

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Related Experiment Video

Updated: May 17, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

A classification scheme for ventricular arrhythmias using wavelets analysis.

K Balasundaram1, S Masse, K Nair

  • 1Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. kbalasun@rnet.ryerson.ca

Medical & Biological Engineering & Computing
|November 8, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using Wavelet analysis to classify ventricular arrhythmias, improving the detection of ventricular tachycardia (VT) and ventricular fibrillation (VF) overlap zones. This enhances implantable cardioverter-defibrillator therapy for sudden cardiac death prevention.

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Last Updated: May 17, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Published on: May 23, 2021

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate identification of ventricular arrhythmias like ventricular tachycardia (VT) and ventricular fibrillation (VF) is crucial for preventing sudden cardiac death.
  • Implantable cardioverter-defibrillators (ICDs) often rely on rate-dependent detection, which can be ambiguous in the overlap zone between VT and VF.
  • Ventricular fibrillation (VF) can exhibit regional organization, complicating the distinction from rhythmic VT.

Purpose of the Study:

  • To develop a method for better categorizing the ambiguous overlap zone between VT and VF.
  • To improve the classification of arrhythmias for more precise therapeutic interventions.
  • To enhance the diagnostic capabilities for patients with complex ventricular arrhythmias.

Main Methods:

  • Utilized Wavelet analysis on surface electrocardiogram (ECG) signals.
  • Employed a two-level binary classifier approach.
  • Analyzed 63 surface ECG signal segments from the MIT-BIH database.

Main Results:

  • Achieved an overall accuracy of 93.7% in extracting VT.
  • Successfully separated organized VF (OVF) from disorganized VF (DVF) with 80.0% accuracy.
  • Demonstrated the effectiveness of Wavelet analysis in classifying arrhythmias within the VT/VF overlap zone.

Conclusions:

  • The proposed Wavelet analysis method offers improved categorization of the VT/VF overlap zone.
  • This approach can assist clinicians in selecting optimal therapeutic strategies for patients.
  • Enhanced arrhythmia classification can lead to more effective prevention of sudden cardiac death.