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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.8K
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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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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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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Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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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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Updated: Aug 15, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.

Yehualashet Megersa Ayano1, Friedhelm Schwenker2, Bisrat Derebssa Dufera1

  • 1Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 11760, Ethiopia.

Diagnostics (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

Interpretable machine learning (IML) offers a path to trustworthy heart disease diagnosis using electrocardiogram (ECG) signals. This review explores IML techniques, datasets, and progress in overcoming challenges in ECG interpretation.

Keywords:
ECGIMLheart diseaseinterpretablemachine learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Heart disease remains a leading global cause of mortality.
  • Electrocardiograms (ECGs) are cost-effective, non-invasive diagnostic tools.
  • Challenges in ECG interpretation include expert scarcity, signal complexity, and comorbidities.

Purpose of the Study:

  • To systematically review interpretable machine learning (IML) techniques for heart disease diagnosis from ECG signals.
  • To address the 'black box' problem of complex machine learning models in clinical practice.
  • To enhance physician trust and enable evidence-based diagnoses using AI.

Main Methods:

  • Systematic literature review of research on IML for ECG-based heart disease diagnosis.
  • Analysis of interpretable machine learning techniques.
  • Identification and characterization of publicly available ECG signal datasets.
  • Assessment of progress in ECG interpretation using IML.

Main Results:

  • Discussion of various interpretable machine learning techniques applicable to ECG data.
  • Cataloging of relevant ECG signal datasets for machine learning tasks.
  • Overview of advancements in ECG interpretation powered by IML.
  • Identification of current limitations and future challenges for IML in this domain.

Conclusions:

  • Interpretable machine learning holds significant promise for improving the accuracy and reliability of heart disease diagnosis from ECGs.
  • Addressing the interpretability gap is crucial for the clinical adoption of AI in cardiology.
  • Further research is needed to overcome existing challenges and fully realize the potential of IML in ECG analysis.