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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

866
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

8.2K
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...
8.2K
Pulse rhythm01:30

Pulse rhythm

922
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...
922
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

3.6K
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....
3.6K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

158
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...
158

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Related Experiment Video

Updated: Sep 6, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

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Novel DERMA Fusion Technique for ECG Heartbeat Classification.

Qurat-Ul-Ain Mastoi1, Teh Ying Wah1, Mazin Abed Mohammed2

  • 1Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.

Life (Basel, Switzerland)
|June 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fusion technique combining dual event-related moving average (DERMA) and fractional Fourier-transform (FrlFT) for accurate electrocardiogram (ECG) analysis. The method achieves over 99.9% accuracy in classifying five types of heartbeats, aiding in cardiac condition detection.

Keywords:
ECG heartbeat classificationECG signal processingcardiovascular diseasefeatures extractionmachine learning

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Area of Science:

  • Cardiology and Biomedical Engineering
  • Signal Processing and Machine Learning

Background:

  • Electrocardiogram (ECG) analysis relies on identifying waveform characteristics (P, QRS, T) and time intervals to detect cardiac abnormalities.
  • Accurate classification of various heartbeats, such as premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), is crucial for diagnosing heart conditions.

Purpose of the Study:

  • To develop and evaluate a novel fusion technique for enhanced identification and classification of abnormal and normal morphological events in ECG signals.
  • To accurately classify five distinct types of heartbeats using advanced signal processing and machine learning models.

Main Methods:

  • Feature extraction was performed on ECG signals to identify key components like P, QRS complex, and T waves.
  • A fusion technique combining dual event-related moving average (DERMA) for peak analysis and fractional Fourier-transform (FrlFT) for time-frequency analysis was proposed.
  • Two supervised learning models, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), were trained for heartbeat classification.
  • Experiments utilized two datasets: MIT-BIH Arrhythmia and the Shaoxing and Ningbo People's Hospital (SPNH) database.

Main Results:

  • The proposed DERMA and FrlFT fusion technique demonstrated high efficacy in identifying abnormal and normal ECG events.
  • The automated model achieved exceptional performance in classifying five types of heartbeats.
  • The system achieved an accuracy of 99.99%, sensitivity of 99.96%, and specificity of 99.9%.

Conclusions:

  • The fusion of DERMA and FrlFT algorithms provides a robust and highly accurate method for ECG signal analysis and cardiac condition classification.
  • The developed automated model, trained with supervised learning techniques, significantly advances the potential for real-time cardiac abnormality detection.
  • The study highlights the effectiveness of combining signal processing and machine learning for precise diagnosis of various arrhythmias.