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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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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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Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

Jiwoong Kim1,2, Sun Jung Lee3, Bonggyun Ko1,4

  • 1Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.

Journal of Korean Medical Science
|February 6, 2024
PubMed
Summary

This study shows that deep learning models can detect atrial fibrillation (AF) using single-lead mobile electrocardiograms (ECG) during normal sinus rhythm (NSR). ResNet50 demonstrated the best performance, highlighting potential for remote arrhythmia screening.

Keywords:
Artificial IntelligenceAtrial FibrillationElectrocardiographyMobile ApplicationsProbability Learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Technology

Background:

  • Mobile devices offer practical single-lead electrocardiogram (ECG) acquisition for arrhythmia detection.
  • Artificial intelligence (AI) improves atrial fibrillation (AF) screening efficiency.
  • Identifying AF during normal sinus rhythm (NSR) using single-lead ECG requires further exploration.

Purpose of the Study:

  • To introduce and evaluate a method for identifying AF using single-lead mobile ECG during NSR.
  • To assess the efficacy of deep learning models in detecting AF from NSR ECG data.

Main Methods:

  • Three deep learning models (RNN, LSTM, ResNet50) were employed.
  • A dataset of 10,287 NSR ECGs was filtered, segmented into 10-second intervals, and preprocessed.
  • Random under-sampling and analysis of 31,767 segments (15,157 masked AF, 16,610 Healthy) were performed.

Main Results:

  • ResNet50 achieved the highest performance: 79.3% recall, 65.8% precision, 71.9% F1-score, 70.5% accuracy, and 0.79 AUC for AF detection from NSR ECGs.
  • RNN and LSTM models showed comparative performance scores of 0.75 and 0.74, respectively.
  • External validation demonstrated ResNet50's F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68.

Conclusions:

  • Deep learning models utilizing single-lead mobile ECG during NSR can effectively identify individuals at risk for AF.
  • Further research is necessary to enhance model performance for widespread clinical application.