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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.
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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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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.
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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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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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Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
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Automated multi-class ECG arrhythmia detection using VMD and multi-task optimization.

Y Murali Krishna1, K Padma Vasavi2, M Krishna Chaitanya3

  • 1ECE, QIS College of Engineering and Technology, Vengamukalapalem, Ongole, 523272, Andhra Pradesh, India.

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Summary
This summary is machine-generated.

This study introduces an advanced framework for classifying cardiac arrhythmias from ECG signals. The optimized feature set significantly improved detection accuracy for conditions like Atrial Fibrillation.

Keywords:
Cardiac arrhythmiasECGMachine-learningMulti-task particle swarm optimization

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate Electrocardiogram (ECG) classification is crucial for diagnosing heart rhythm disorders.
  • Existing methods may face challenges in distinguishing between various complex arrhythmias.
  • The need for robust and efficient ECG analysis frameworks remains high in clinical practice.

Purpose of the Study:

  • To develop and evaluate a novel multi-class ECG classification framework.
  • To identify four key cardiac conditions: Atrial Fibrillation (AF), Ventricular Fibrillation (VF), Normal Rhythm (NR), and Ventricular Tachycardia (VT).
  • To enhance feature discriminability and reduce computational load through optimized feature selection.

Main Methods:

  • ECG signals were processed using Variational Mode Decomposition (VMD).
  • Higher-order statistics and entropy-based features were extracted from decomposed modes.
  • Multi-task Particle Swarm Optimization (MT-PSO) was utilized for feature selection and reduction.
  • Several machine learning models, including LightGBM, HistGradientBoost, XGBoost, and ExtraTrees, were evaluated.

Main Results:

  • The optimized feature set significantly improved classification performance across all evaluated models.
  • LightGBM achieved the highest accuracy (0.993), followed closely by HistGradientBoost (0.991), XGBoost (0.990), and ExtraTrees (0.990).
  • Execution time was reduced for several models post-optimization, indicating increased efficiency.
  • Confusion matrix and ROC analyses confirmed reliable detection of all four cardiac classes.

Conclusions:

  • The proposed VMD-based feature extraction and MT-PSO optimization framework offers a highly effective approach for multi-class ECG classification.
  • The framework demonstrates competitive or superior performance compared to existing methods for detecting cardiac arrhythmias.
  • This approach holds promise for improving the accuracy and efficiency of automated cardiac diagnosis.