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Related Concept Videos

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

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

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...
Instrumentation Amplifier01:25

Instrumentation Amplifier

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...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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...
Electrocardiogram01:29

Electrocardiogram

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 the T...
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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Related Experiment Videos

Integration of Machine Learning Techniques in ECG-Based Multiclass Arrhythmia Classification with Explainability

Abdullah1,2, Zulaikha Fatima3, Abdollah Abadian1

  • 1Center for Computing Research, Instituto Politecnico Nacional (IPN), Mexico City 07320, Mexico.

Biosensors
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study compares deep learning models for electrocardiogram (ECG) analysis, developing a Fine-Tuned CNN (FT-CNN) that achieves 98.51% accuracy in detecting cardiac arrhythmias.

Keywords:
ECG signalscomputer-aided diagnosis systemsconvolutional neural networksdeep learningdeep neural networksmachine learning

Related Experiment Videos

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias, a major cause of global mortality.
  • Deep learning has advanced automated arrhythmia classification, but lacks standardized comparisons and interpretability.
  • Existing studies often fail to provide fair comparisons of fundamental neural network architectures under unified conditions.

Purpose of the Study:

  • To conduct a rigorous comparative analysis of Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Residual Network (ResNet) for ECG arrhythmia classification.
  • To develop and optimize an efficient Fine-Tuned CNN (FT-CNN) tailored for ECG signal characteristics.
  • To enhance model interpretability using Grad-CAM and Integrated Gradients.

Main Methods:

  • Comparative analysis of ANN, CNN, and ResNet on the MIT-BIH Arrhythmia Database using identical protocols.
  • Development of an FT-CNN with adaptive kernel sizing, multi-faceted regularization, cosine annealing learning rate, and a custom loss function.
  • Integration of Grad-CAM and Integrated Gradients for model explainability, with quantitative evaluation of attribution faithfulness.

Main Results:

  • The proposed FT-CNN achieved a high accuracy of 98.51%, surpassing fourteen benchmark models including standard CNN (97.20%) and ResNet (96.88%).
  • Ablation studies demonstrated a significant 6.17% improvement over the baseline model.
  • Excellent class-wise F1-scores were observed for normal (0.99), ventricular ectopic (0.95), and unknown (0.98) beats, though supraventricular ectopic and fusion beats remain challenging.

Conclusions:

  • The FT-CNN demonstrates superior performance in automated ECG arrhythmia classification compared to existing models.
  • The study provides a standardized framework for comparing deep learning architectures in ECG analysis.
  • Integrated explainability methods offer valuable insights into model decision-making for cardiac arrhythmia detection.