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Related Concept Videos

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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Correlation between ECG and Cardiac Cycle01:25

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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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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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ECG Interpretation of Rhythms01:24

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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.
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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.
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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine.

Kandala N V P S Rajesh1, Ravindra Dhuli1

  • 1School of Electronics Engineering, VIT University, Vellore 632014, India.

Computers in Biology and Medicine
|June 19, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces novel feature extraction methods for classifying electrocardiogram (ECG) heartbeats, significantly improving arrhythmia risk prediction accuracy. The ensemble empirical mode decomposition (EEMD) approach achieved high sensitivity and specificity.

Keywords:
ArrhythmiaClassificationECG signalEmpirical mode decompositionEnsemble empirical mode decompositionSMO-SVM

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning in Healthcare

Background:

  • Accurate classification of electrocardiogram (ECG) heartbeats is crucial for predicting cardiac arrhythmias.
  • Minute variations in ECG signal morphology present significant challenges for traditional classification methods.
  • Existing methods often struggle with the complexity and subtle differences in various heartbeat types.

Purpose of the Study:

  • To develop and evaluate two novel feature extraction techniques for classifying five types of heartbeats.
  • To assess the efficacy of these methods in predicting arrhythmic risk using standard ECG databases.
  • To compare the performance of the proposed approaches against existing state-of-the-art methods.

Main Methods:

  • Two feature extraction approaches were proposed: Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD).
  • Features extracted include sample entropy, coefficient of variation, singular values, and band power of intrinsic mode functions (IMFs).
  • Classifications were performed using a sequential minimal optimization-support vector machine (SMO-SVM) on the MIT-BIH and INCART databases.

Main Results:

  • The EEMD-based approach achieved high performance: 98.01% sensitivity, 99.49% specificity, and 99.20% accuracy on the MIT-BIH database.
  • For the INCART database, the EEMD approach yielded 95.15% sensitivity, 98.37% specificity, and 97.57% accuracy.
  • Both proposed methods demonstrated superior performance compared to existing techniques in terms of accuracy, sensitivity, and specificity.

Conclusions:

  • The proposed feature extraction methods, particularly using EEMD, offer a significant advancement in ECG heartbeat classification for arrhythmia risk prediction.
  • These techniques effectively capture subtle signal variations, leading to improved diagnostic accuracy.
  • The study highlights the potential of advanced signal decomposition and machine learning for enhanced cardiovascular diagnostics.