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Electrocardiogram

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...

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Related Experiment Video

Updated: Dec 7, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

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Published on: December 28, 2012

Detecting myocardial scar using electrocardiogram data and deep neural networks.

Nils Gumpfer¹, Dimitri Grün², Jennifer Hannig¹

¹Cognitive Information Systems, KITE-Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, 61169 Friedberg, Germany.

Biological Chemistry

|October 2, 2020

Summary

This study introduces an artificial intelligence deep learning model to predict myocardial scar using electrocardiograms (ECG) and clinical data. The AI model shows promising diagnostic precision for detecting scar tissue, potentially improving screening for ischaemic heart disease.

Keywords:

ECG classification artificial intelligence convolutional neural networks deep learning ischaemic heart disease myocardial scar

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Last Updated: Dec 7, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

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Published on: December 28, 2012

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Published on: April 11, 2025

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Area of Science:

Cardiology
Artificial Intelligence
Medical Diagnostics

Background:

Ischaemic heart disease is a leading cause of death globally.
Early detection of myocardial pathologies, such as scar tissue, improves treatment outcomes.
Current diagnostic methods like MRI are costly and have limited availability.

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI) based deep learning model for predicting myocardial scar.
To utilize electrocardiogram (ECG) data and clinical parameters for scar detection.
To offer a more accessible and cost-effective screening method for myocardial scar.

Main Methods:

A deep learning model was developed for myocardial scar prediction.
The model was trained and evaluated using a 6-fold cross-validation approach.
Input data included 12-lead ECG time series and clinical parameters.

Main Results:

The AI model achieved an area under the curve (AUC) of 0.89.
Sensitivity, specificity, and accuracy were reported as 70.0%, 84.3%, and 78.0%, respectively.
The model demonstrated high diagnostic precision in predicting myocardial scar.

Conclusions:

AI-powered ECG analysis shows significant potential for myocardial scar detection.
This approach may offer a novel, accessible, and cost-effective screening tool.
Further development could support early diagnosis and management of ischaemic heart disease.