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Related Concept Videos

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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Correlation between ECG and Cardiac Cycle01:25

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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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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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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model.

Emrah Irmak1

  • 1Electrical-Electronics Engineering Department, Alanya Alaaddin Keykubat University, 07425, Alanya, Antalya, Turkey. emrah.irmak@alanya.edu.tr.

Physical and Engineering Sciences in Medicine
|January 12, 2022
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Summary

This study introduces a novel deep learning model using electrocardiogram (ECG) images for rapid COVID-19 diagnosis. The method accurately detects cardiovascular abnormalities associated with COVID-19, aiding pandemic control.

Keywords:
COVID-19 diagnosisCardiovascular diseases diagnosisConvolutional neural networksDeep learningElectrocardiographyMachine learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • COVID-19 impacts the cardiovascular system, necessitating improved diagnostic tools beyond RT-PCR and imaging.
  • Current diagnostic methods for COVID-19 have limitations in sensitivity and cost.
  • Early detection of COVID-19's cardiovascular effects via electrocardiograms (ECG) is crucial.

Purpose of the Study:

  • To develop and validate a novel deep Convolutional Neural Network (CNN) model for diagnosing COVID-19 using ECG trace images.
  • To assess the model's ability to detect cardiovascular abnormalities linked to COVID-19.

Main Methods:

  • A deep CNN model was designed to analyze ECG trace images derived from COVID-19 patients.
  • The model was trained and tested on ECG data to classify various conditions, including COVID-19, normal heartbeats, and myocardial infarction.

Main Results:

  • Achieved high binary classification accuracies: 98.57% (COVID-19 vs. Normal), 93.20% (COVID-19 vs. Abnormal Heartbeats), and 96.74% (COVID-19 vs. Myocardial Infarction).
  • Demonstrated strong multi-classification performance: 86.55% and 83.05% for complex diagnostic tasks.
  • Attained excellent Area Under the Curve (AUC) values, indicating robust diagnostic capability.

Conclusions:

  • The proposed ECG-based deep learning model shows significant potential for rapid and accurate COVID-19 diagnosis.
  • This approach can expedite patient diagnosis and treatment, optimize clinician workflow, and aid in pandemic management.