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Related Concept Videos

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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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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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

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Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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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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Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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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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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Advancing cardiac diagnostics: high-accuracy arrhythmia classification with the EGOLF-net model.

Deepika Tenepalli1, T M Navamani1

  • 1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.

Frontiers in Physiology
|July 14, 2025
PubMed
Summary

A new model, EGOLFNet, accurately detects heart arrhythmias using electrocardiogram (ECG) data. This Enhanced Gray Wolf Optimization with LSTM Fusion Network achieves 99.61% accuracy, improving cardiac diagnostic systems.

Keywords:
ECGLSTMarrhythmiagray wolf optimizationheart diseaseoptimization

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Signal Processing

Background:

  • Arrhythmia detection is critical for managing heart rhythm disturbances.
  • Existing methods require improvement in accuracy and robustness.

Purpose of the Study:

  • To develop and validate a novel deep learning model, EGOLFNet, for accurate arrhythmia classification.
  • To enhance diagnostic capabilities in cardiology using advanced AI techniques.

Main Methods:

  • Utilized the MIT-BIH Arrhythmia Database for training and validation.
  • Employed Enhanced Gray Wolf Optimization for optimal feature selection.
  • Integrated LSTM layers to capture temporal dependencies in ECG signals.
  • Preprocessed ECG data including normalization and noise filtering.

Main Results:

  • Achieved a high accuracy of 99.61% in arrhythmia classification.
  • Demonstrated the effectiveness of EGOLFNet in identifying heart arrhythmias.
  • Showcased the model's reliability and robustness.

Conclusions:

  • EGOLFNet represents a significant advancement in automated arrhythmia detection.
  • The model shows potential for integration into clinical cardiology diagnostic systems.
  • This approach offers a highly reliable tool for timely medical intervention.