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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.4K
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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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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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Related Experiment Video

Updated: Jul 10, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class

Mohamed Elgendi1, Kirina van der Bijl1, Carlo Menon1

  • 1Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland.

Diagnostics (Basel, Switzerland)
|November 24, 2023
PubMed
Summary
This summary is machine-generated.

High-quality electrocardiogram (ECG) diagnostics are vital for cardiovascular health. Our new model automates ECG quality assessment, achieving high accuracy even with imbalanced data, improving clinical diagnostics.

Keywords:
CNN-LSTM modelartificial intelligencedigital healthdigital medicineelectrocardiograms (ECGs)user-friendly toolbox

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Cardiovascular diseases are rising, increasing the need for accurate electrocardiogram (ECG) diagnostics.
  • High-quality ECG recordings are crucial for reliable diagnostic interpretation.
  • Automated ECG quality assessment is needed to improve efficiency and accuracy in clinical settings.

Purpose of the Study:

  • To develop and evaluate a CNN-LSTM model for automated ECG quality assessment.
  • To assess the model's performance across datasets with varying signal quality ratios.
  • To investigate the impact of training data class distribution on model reliability.

Main Methods:

  • Developed a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model.
  • Trained the model on balanced datasets collected in clinical settings, including real noise sources.
  • Evaluated the model using three datasets with different ratios of acceptable to unacceptable ECG signals.

Main Results:

  • The CNN-LSTM model achieved high F1 scores, ranging from 95.87% to 98.40%, across diverse datasets.
  • Training with real noise sources demonstrated superior applicability in real-life scenarios compared to simulations.
  • A significant decrease in F1 score was observed when class representation shifted from balanced to imbalanced ratios.

Conclusions:

  • The developed CNN-LSTM model provides a practical and accurate solution for automated ECG quality assessment.
  • The study highlights the critical importance of balanced class representation in training and testing ECG quality models.
  • Findings emphasize the need for careful consideration of data distribution in future research for reliable AI-driven diagnostic tools.