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Related Concept Videos

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...
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...
Cardiac Action Potential01:30

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials
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...

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Related Experiment Video

Updated: May 17, 2026

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

Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural

Fabienne Porée1, Amar Kachenoura, Guy Carrault

  • 1INSERM, U1099, Rennes, F-35000, France. fabienne.poree@univ-rennes1.fr

IEEE Transactions on Bio-Medical Engineering
|October 23, 2012
PubMed
Summary

This study introduces advanced artificial neural network methods to create 12-lead electrocardiograms (ECG) from implantable device signals, improving remote cardiac patient monitoring.

Related Experiment Videos

Last Updated: May 17, 2026

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
  • Cardiology
  • Artificial Intelligence

Background:

  • Remote monitoring of cardiac patients with implantable devices is crucial for timely intervention.
  • Synthesizing surface electrocardiograms (ECG) from intracardiac electrograms (EGM) can enhance remote follow-up capabilities.

Purpose of the Study:

  • To develop and compare novel methods for synthesizing 12-lead surface ECG from intracardiac electrograms (EGM).
  • To evaluate the efficacy of dynamic time-delay artificial neural networks (TDNNs) against traditional linear approaches for ECG synthesis.

Main Methods:

  • Proposed two TDNN-based methods: a direct approach estimating 12 transfer functions and an indirect approach using orthogonalization with three transfer functions.
  • Evaluated methods on data from 15 cardiac patients, comparing synthesized ECG with actual ECGs.
  • Assessed performance based on correlation coefficients, extracted feature comparison, and cardiologist qualitative analysis.

Main Results:

  • TDNN-based methods demonstrated superior performance in synthesizing 12-lead ECG compared to linear methods.
  • The indirect TDNN method proved efficient, requiring fewer transfer functions.
  • Results were validated across different EGM configurations and through expert clinical assessment.

Conclusions:

  • Dynamic time-delay artificial neural networks offer an efficient and effective solution for synthesizing 12-lead ECG from intracardiac signals.
  • These methods hold significant potential for improving remote patient monitoring in cardiology.
  • The study validates the clinical relevance of AI-driven ECG synthesis for cardiac care.