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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.9K
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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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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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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....
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks.

Thao Nguyen1, Hieu H Pham1,2, Khiem H Le1

  • 1College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam.

Plos One
|November 4, 2022
PubMed
Summary

This study developed a novel method to digitize electrocardiogram (ECG) paper records for COVID-19 detection. A deep learning model accurately diagnosed COVID-19 using these digitized ECG signals, showing potential for rapid screening.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • The COVID-19 pandemic highlighted global healthcare system vulnerabilities, necessitating rapid and cost-effective diagnostic tools.
  • Cardiac injury is a reported complication of COVID-19, suggesting electrocardiograms (ECG) as potential biomarkers.
  • Existing diagnostic methods for COVID-19 can be time-consuming and resource-intensive.

Purpose of the Study:

  • To develop an automated system for COVID-19 detection using electrocardiogram (ECG) signals.
  • To propose and validate a novel method for extracting usable ECG data from paper records.
  • To assess the efficacy of a deep learning model in diagnosing COVID-19 from digitized ECG signals.

Main Methods:

  • A novel technique was developed to digitize ECG signals from traditional paper records.
  • The quality of digitized signals was evaluated by comparing R-peak and RR-interval measurements against original paper ECGs.
  • A one-dimensional convolutional neural network (1D-CNN), specifically SEResNet18, was trained on the digitized ECG signals for classification.

Main Results:

  • The proposed digitization method accurately captured original ECG signals, achieving a mean absolute error of 28.11 ms.
  • The SEResNet18 model demonstrated high accuracy in classifying COVID-19: 98.42% (vs. Normal) and 98.50% (vs. other classes).
  • The system also performed effectively in multi-classification tasks, indicating robust diagnostic capability.

Conclusions:

  • Digitized ECG signals can reliably represent original cardiac electrical activity.
  • A deep learning system trained on digitized ECGs shows significant potential as a tool for automated COVID-19 diagnosis.
  • This approach offers a promising avenue for rapid, cost-effective, and accessible COVID-19 screening and diagnosis.