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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...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
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...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...
Bode Plots Construction01:24

Bode Plots Construction

The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...

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Related Experiment Video

Updated: Jun 17, 2026

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

Mathematical modeling of electrocardiograms: a numerical study.

Muriel Boulakia1, Serge Cazeau, Miguel A Fernández

  • 1Laboratoire Jacques-Louis Lions, Université Pierre et Marie Curie-Paris 6, UMR 7598, 75005, and Département de Rythmologie-Stimulation, Hôpital Saint-Joseph, 185, rue Raymond Losserand, 75014 Paris, France.

Annals of Biomedical Engineering
|December 25, 2009
PubMed
Summary

This study presents a mathematical model for simulating realistic 12-lead electrocardiograms (ECG) using partial differential equations. The model accurately reproduces ECG amplitudes, shapes, and polarities, advancing cardiac electrophysiology simulation.

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In Silico Clinical Trials for Cardiovascular Disease
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In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

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Last Updated: Jun 17, 2026

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

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

  • Computational Biology and Physiology
  • Medical Imaging and Simulation
  • Mathematical Modeling in Medicine

Background:

  • Accurate electrocardiogram (ECG) simulation is crucial for understanding cardiac function and diagnosing heart conditions.
  • Existing models often struggle to reproduce the complexity and realism required for clinical applications.
  • Developing sophisticated mathematical models is essential for advancing diagnostic capabilities in cardiology.

Purpose of the Study:

  • To develop a novel mathematical model for generating realistic 12-lead ECGs.
  • To integrate advanced physiological and anatomical features into a comprehensive cardiac simulation framework.
  • To validate the model's accuracy and efficiency using state-of-the-art numerical techniques.

Main Methods:

  • Utilized partial differential equations, specifically the bidomain equations for cardiac tissue and a generalized Laplace equation for the torso.
  • Incorporated key physiological factors: anisotropic conductivity, cellular heterogeneity, and His bundle activation.
  • Employed advanced numerical methods: domain decomposition and second-order semi-implicit time-marching schemes for efficient computation.

Main Results:

  • The numerical simulation successfully produced realistic 12-lead ECGs with accurate amplitudes, shapes, and polarities.
  • The model demonstrated a good balance between computational accuracy, stability, and efficiency.
  • Sensitivity analysis of model parameters was performed to understand their impact on ECG output.

Conclusions:

  • The proposed mathematical model provides a robust framework for realistic ECG simulation.
  • The detailed inclusion of physiological factors and advanced numerical methods enhances the clinical relevance of simulated ECGs.
  • This approach offers a valuable tool for research, education, and potentially clinical decision-making in cardiology.