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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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Instrumentation Amplifier01:25

Instrumentation Amplifier

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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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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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Related Experiment Video

Updated: Jan 8, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Advances in machine and deep learning for ECG beat classification: a systematic review.

Allam Jaya Prakash1, Abdelkader Nasreddine Belkacem2, Ibrahim M Elfadel3

  • 1Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University, Abu Dhabi, United Arab Emirates.

Frontiers in Digital Health
|December 15, 2025
PubMed
Summary

This review explores artificial intelligence (AI) for electrocardiogram (ECG) beat classification, highlighting advancements in automated analysis and identifying challenges like data imbalance for future research in cardiac diagnostics.

Keywords:
arrhythmiaclassificationdeep learningelectrocardiogramfeature extractionmachine learning

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Area of Science:

  • Cardiology and Medical Informatics
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Electrocardiogram (ECG) is a vital, non-invasive tool for cardiac assessment.
  • Artificial intelligence (AI) is increasingly used for automated ECG analysis.
  • ECG beat classification is crucial for accurate cardiac diagnostics.

Purpose of the Study:

  • To systematically review advancements in AI-driven ECG beat classification from 2014-2024.
  • To analyze the evolution from traditional to deep learning methods.
  • To identify current challenges and propose future research directions.

Main Methods:

  • Systematic literature review adhering to PRISMA criteria.
  • Analysis of 106 articles published between 2014 and 2024.
  • Focus on pre-processing, feature engineering, and AI model architectures.

Main Results:

  • Shift from manual feature engineering to automated feature extraction using CNNs, RNNs, and hybrid models.
  • Identified challenges: data imbalance, inter-patient variability, lack of standardized metrics.
  • Emerging trends include attention mechanisms and deep learning architectures.

Conclusions:

  • AI significantly enhances ECG beat classification accuracy and efficiency.
  • Standardized datasets, cross-modal fusion, and interpretable AI are key for clinical translation.
  • Future work should focus on addressing current limitations for real-world deployment.