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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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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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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: Nov 12, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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HeartNetEC: a deep representation learning approach for ECG beat classification.

Sri Aditya Deevi1, Christina Perinbam Kaniraja1, Vani Devi Mani1

  • 1Department of Avionics, Indian Institute of Space Science and Technology, Thiruvananthapuram, India.

Biomedical Engineering Letters
|March 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning approach for electrocardiogram (ECG) analysis, improving the accuracy of cardiac abnormality detection. The method efficiently classifies ECG heartbeats, aiding cardiologists in diagnosing cardiovascular diseases.

Keywords:
Beat classificationDeep learningDenoisingECGHeartNetECsegmentation

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electrocardiograms (ECGs) are vital for assessing cardiovascular health.
  • Accurate ECG interpretation is crucial for diagnosing cardiovascular diseases (CVDs).
  • Beat-wise ECG classification aids in identifying cardiac abnormalities.

Purpose of the Study:

  • To propose an efficient deep representation learning approach for ECG beat classification.
  • To reduce the time and burden on cardiologists for ECG analysis.
  • To develop a two-stage system for ECG denoising and beat classification.

Main Methods:

  • A deep learning-based denoising block to process raw ECG signals.
  • A deep learning-based beat classification block for categorizing heartbeats.
  • Testing on the PhysioNet MIT-BIH Arrhythmia Database for ten heartbeat types.

Main Results:

  • The proposed deep learning approach demonstrated high efficiency in ECG beat classification.
  • The system achieved superior performance on relevant metrics for identifying different heartbeat types.
  • Meaningful predictions were made, indicating the approach's effectiveness.

Conclusions:

  • The developed deep representation learning method offers an efficient solution for ECG beat classification.
  • This approach can significantly assist cardiologists in analyzing ECGs and diagnosing CVDs.
  • The study highlights the potential of deep learning in advancing cardiovascular diagnostics.