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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

431
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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Electrocardiogram01:29

Electrocardiogram

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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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

270
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....
270
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

1.6K
The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
1.6K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

150
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
150
Correlation between ECG and Cardiac Cycle01:24

Correlation between ECG and Cardiac Cycle

2.7K
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 10, 2025

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

179

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and

Gi-Won Yoon1, Segyeong Joo1

  • 1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Methodsx
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

Transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images improves classification accuracy for conditions like Atrial Fibrillation (AFib) and Left Ventricular Hypertrophy (LVH). The 512x512 resolution offers optimal efficiency and performance.

Keywords:
Atrial fibrillationClassificationDeep LearningEGAFCovNextElectrocardiogramGramian angular field

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

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Conventional electrocardiogram (ECG) analysis faces limitations due to time inefficiencies and potential for human error.
  • Feature-based ECG analysis can be complex and may not capture all relevant signal characteristics.
  • Developing automated and accurate ECG classification methods is crucial for timely diagnosis.

Purpose of the Study:

  • To investigate the efficacy of transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images for enhanced classification.
  • To evaluate the performance of different GAF image resolutions (5000x5000, 512x512, 256x256) in classifying four ECG categories.
  • To assess the impact of segmentation methods on ECG classification accuracy using deep learning.

Main Methods:

  • 1D ECG signals were converted into 2D GAF images at resolutions of 5000x5000, 512x512, and 256x256 pixels.
  • Segmentation techniques were applied to improve feature localization within the GAF images.
  • The ConvNext deep learning model was utilized for image classification, with performance metrics including accuracy, precision, recall, and F1-score.

Main Results:

  • The 512x512 GAF image resolution demonstrated an optimal balance between computational efficiency and classification accuracy.
  • Achieved F1-scores were 0.781 for Atrial Fibrillation (AFib), 0.71 for Left Ventricular Hypertrophy (LVH), 0.521 for Right Ventricular Hypertrophy (RVH), and 0.792 for Normal ECG.
  • Segmentation methods enhanced classification performance, particularly for detecting LVH and RVH.
  • The 5000x5000 resolution yielded high accuracy but was computationally intensive; the 256x256 resolution suffered from detail loss and reduced accuracy.

Conclusions:

  • GAF transformation offers a promising approach for improving ECG signal analysis and classification.
  • The 512x512 resolution provides a practical and effective setting for deep learning-based ECG classification.
  • Integrating segmentation with GAF images can further boost diagnostic accuracy for specific cardiac conditions.