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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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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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ECGEFNet: A two-branch deep learning model for calculating left ventricular ejection fraction using

Yiqiu Qi1, Guangyuan Li2, Jinzhu Yang1

  • 1Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China.

Artificial Intelligence in Medicine
|January 14, 2025
PubMed
Summary

A novel deep learning model, ECGEFNet, uses electrocardiograms (ECG) to calculate left ventricular ejection fraction (LVEF), aiding early detection of left ventricular systolic dysfunction (LVSD). This tool offers potential for primary care screening and continuous cardiac monitoring.

Keywords:
Deep learningElectrocardiogramFusion attentionLeft ventricular ejection fraction

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Left ventricular systolic dysfunction (LVSD) significantly impacts cardiovascular disease prognosis.
  • Accurate Left Ventricular Ejection Fraction (LVEF) assessment is crucial for monitoring cardiac function.
  • Current echocardiography methods for LVEF lack accessibility in primary care and real-time monitoring capabilities.

Purpose of the Study:

  • To develop a deep learning model (ECGEFNet) for calculating LVEF directly from electrocardiogram (ECG) data.
  • To establish a potential primary medical screening tool for early detection and dynamic monitoring of cardiac dysfunction.
  • To enhance feature fusion and information interaction between different data representations within the model.

Main Methods:

  • A two-branch deep learning architecture (ECGEFNet) was designed to process both numerical ECG signals and waveform plots.
  • An innovative fusion attention mechanism (FAT) and a two-branch feature fusion module (BFF) were developed to optimize feature learning and integration.
  • The model was trained and validated on a large internal dataset.

Main Results:

  • ECGEFNet achieved an accuracy of 92.3% for cardiac dysfunction screening.
  • The model demonstrated a mean absolute error (MAE) of 4.57% in LVEF calculation.
  • The proposed model outperformed existing baseline models in performance.

Conclusions:

  • ECGEFNet shows significant promise as a non-invasive tool for LVEF calculation using ECG.
  • The model facilitates early detection and real-time monitoring of cardiac functional impairments, particularly LVSD.
  • This approach holds great potential for improving cardiovascular disease management in primary care settings.