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Electrocardiogram Fundamentals

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...

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Related Experiment Video

Updated: Jun 12, 2025

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

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

Published on: January 8, 2013

PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.

Thomas Grandits^1,2, Simone Pezzuto³, Jolijn M Lubrecht³

¹Institute of Computer Graphics and Vision Graz University of Technology.

Statistical Atlases and Computational Models of the Heart. STACOM (Workshop)

|September 23, 2024

Summary

This summary is machine-generated.

This study introduces PIEMAP, a new method to determine cardiac tissue properties from electroanatomical mapping data. PIEMAP accurately infers fiber orientation and conduction velocity for personalized heart models.

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Last Updated: Jun 12, 2025

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

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

Published on: January 8, 2013

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Cardiac Electrophysiology
Computational Biology
Medical Imaging

Background:

Electroanatomical mapping provides detailed local electrical data of cardiac tissue.
Personalized cardiac models require accurate patient-specific parameters, which are challenging to obtain.
Current methods struggle with inferring complex tissue properties like conductivity anisotropy.

Purpose of the Study:

To develop a novel inverse problem method for inferring cardiac conductivity tensor structure.
To extract fiber orientation and conduction velocity from electroanatomical mapping data.
To enable more accurate patient-specific cardiac modeling for personalized therapies.

Main Methods:

Introduction of PIEMAP (Parameter Identification for Eikonal Models using Electroanatomical Mapping), a novel inverse problem approach.
Application of PIEMAP to an eikonal model of cardiac activation.
Validation using both synthetic and clinical electroanatomical mapping data.

Main Results:

PIEMAP demonstrated robust performance with synthetic datasets.
The method showed promising results when applied to clinical data.
Successful inference of anisotropic conductivity tensor components, including fiber orientation and conduction velocities.

Conclusions:

PIEMAP offers a reliable method for inferring cardiac tissue structure from electroanatomical mapping.
The findings suggest PIEMAP's potential as a valuable tool in clinical workflows for personalized cardiac therapies.
This approach could significantly enhance the accuracy of patient-specific cardiac models.