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Related Concept Videos

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...
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...
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):
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...
Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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...

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Updated: Jul 1, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

[EM algorithm for the inverse problem of electrocardiography].

Fei Gao1, Huafeng Liu

  • 1Stat Key Laboratory of Modern Optical Instrument, Zhejiang University, Hangzhou 310027, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|September 16, 2008
PubMed
Summary
This summary is machine-generated.

This study enhances epicardial potential calculation for electrocardiography (ECG) using an Expectation Maximization (EM) algorithm. The EM algorithm improves solution convergence and reduces errors compared to traditional Kalman filtering in ECG inverse problems.

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Last Updated: Jul 1, 2026

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

Published on: May 27, 2022

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:

  • Biomedical Engineering
  • Computational Electrophysiology
  • Medical Imaging

Context:

  • The inverse problem of electrocardiography (ECG) aims to determine epicardial potentials from body-surface potential measurements.
  • Accurate epicardial potentials are crucial for diagnosing cardiac pathologies and guiding clinical applications.
  • Existing methods face challenges with parameter uncertainty.

Purpose:

  • To develop a robust method for calculating epicardial potentials by addressing parameter uncertainty in the ECG inverse problem.
  • To construct a 2D human torso model using the Finite Element Method (FEM) for simulating ECG forward and inverse problems.
  • To implement and evaluate the Expectation Maximization (EM) algorithm for parameter estimation.

Summary:

  • A 2D FEM human torso model was created to represent the relationship between body surface and epicardial potentials.
  • The Expectation Maximization (EM) algorithm, incorporating a Kalman filter, was designed to handle parameter uncertainty.
  • The EM algorithm iteratively estimates parameters through Expectation (Kalman filter) and Maximization (likelihood function) steps.

Impact:

  • Simulations demonstrate that the EM algorithm achieves superior convergence and significantly smaller relative errors compared to traditional Kalman filtering.
  • This improved accuracy in epicardial potential calculation holds potential for enhanced diagnostic capabilities in clinical cardiology.
  • The study provides a more reliable computational approach for the challenging ECG inverse problem.