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Related Concept Videos

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...
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase of...
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.
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...
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.
Electrocardiogram01:29

Electrocardiogram

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 the T...

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Related Experiment Video

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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

An artificial vector model for generating abnormal electrocardiographic rhythms.

Gari D Clifford1, Shamim Nemati, Reza Sameni

  • 1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. gari@robots.ox.ac.uk

Physiological Measurement
|March 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced artificial model for simulating abnormal cardiac rhythms using vectorcardiogram (VCG) data. The model accurately captures beat variations and physiological changes, enhancing electrocardiogram (ECG) analysis.

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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Related Experiment Videos

Last Updated: Jun 14, 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

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Area of Science:

  • Computational biology
  • Biomedical engineering
  • Cardiovascular research

Background:

  • Existing artificial models for multi-channel electrocardiogram (ECG) generation require enhancement for simulating abnormal cardiac rhythms.
  • Accurate simulation of cardiac arrhythmias is crucial for understanding disease mechanisms and testing diagnostic tools.

Purpose of the Study:

  • To present generalized artificial models for simulating abnormal cardiac rhythms based on three-dimensional vectorcardiogram (VCG) formulation.
  • To incorporate beat-to-beat morphology changes, QT-heart rate (HR) hysteresis, and respiration effects into cardiac rhythm simulations.

Main Methods:

  • Utilized a three-dimensional VCG formulation with Gaussian kernels fitted to real VCG recordings to generate normal cardiac dipoles.
  • Modeled abnormal beats as perturbations or new trajectories, with beat type switching governed by a first-order Markov chain.
  • Incorporated beat-to-beat variability by adjusting dipole angular frequency based on RR intervals and simulated QT-HR hysteresis and respiration effects.

Main Results:

  • Successfully simulated heart rate (HR)-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy.
  • Demonstrated the model's capability to simulate natural morphology changes and physiological influences like respiration.
  • Revealed previously unreported effects of common TWA estimation methods through model application.

Conclusions:

  • The generalized artificial model provides a robust platform for simulating complex abnormal cardiac rhythms and their underlying physiological mechanisms.
  • The model's ability to incorporate various dynamic changes enhances its utility in ECG research and the development of diagnostic algorithms.
  • Further applications of this model can lead to a deeper understanding of cardiac electrophysiology and improve TWA analysis techniques.