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Related Concept Videos

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...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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...
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):

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Related Experiment Video

Updated: Jun 10, 2026

Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model.

Omid Sayadi1, Mohammad B Shamsollahi, Gari D Clifford

  • 1Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran. osayadi@ee.sharif.edu

Physiological Measurement
|August 20, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Gaussian wave model for generating synthetic electrocardiogram (ECG) signals and characteristic waves. The model also effectively denoises ECGs using a Bayesian framework, improving signal quality for arrhythmias.

Related Experiment Videos

Last Updated: Jun 10, 2026

Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Cardiology

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions.
  • Accurate modeling and denoising of ECGs are essential for reliable analysis.
  • Generating realistic synthetic ECGs aids in training and testing diagnostic algorithms.

Purpose of the Study:

  • To develop a Gaussian wave-based state space model for ECG signal dynamics.
  • To utilize the model for generating synthetic ECGs and characteristic waves (P, QRS, T).
  • To implement a model-based Bayesian framework for ECG denoising, including arrhythmias.

Main Methods:

  • A Gaussian wave-based state space model was formulated to capture ECG temporal dynamics.
  • Separate state variables were assigned to each characteristic wave (P, QRS, T).
  • Discrete model equations were used within an extended Kalman filter and smoother for denoising.

Main Results:

  • The model successfully generated individual synthetic characteristic waves and realistic ECG signals, including arrhythmias.
  • The Bayesian denoising framework achieved a maximum signal-to-noise ratio (SNR) improvement of 12.7 dB.
  • The denoising approach minimized clinically relevant distortions, showing robustness across various input SNRs.

Conclusions:

  • The Gaussian wave-based state space model provides an effective framework for synthetic ECG generation.
  • Model-based Bayesian filtering offers a superior method for denoising noisy ECG recordings.
  • This integrated approach supports both realistic ECG synthesis and accurate signal processing for clinical applications.