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Related Concept Videos

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...
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
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.
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...
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...
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 Video

Updated: Jun 10, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

A Computationally Efficient Hybrid Approach for Electrocardiogram-Based Arrhythmia Prediction.

Manjesh B N1, Raja Praveen K N2, Azadeh Amoozegar3

  • 1JAIN (Deemed-to-be-University); manjeshbn@gmail.com.

Journal of Visualized Experiments : Jove
|June 8, 2026
PubMed
Summary

A new deep learning model accurately detects five types of arrhythmias using electrocardiogram (ECG) signals. This AI system offers high precision for early cardiovascular disease diagnosis and personalized digital healthcare.

Related Experiment Videos

Last Updated: Jun 10, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cardiovascular diseases, particularly arrhythmias, are a significant global cause of mortality.
  • Early detection and diagnosis of arrhythmias are crucial for effective patient management.
  • Automated systems are needed to assist in the timely identification of cardiac irregularities.

Purpose of the Study:

  • To develop and validate a deep learning model for accurate arrhythmia detection using electrocardiogram (ECG) signals.
  • To classify five distinct types of heartbeats: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V).
  • To assess the model's performance against existing state-of-the-art methods.

Main Methods:

  • Utilized Lead I ECG signals from multiple large-scale databases (MIT-BIH Arrhythmia, Supraventricular, INCART 12-lead, Sudden Cardiac Death Holter).
  • Preprocessed data by segmenting into 180-sample windows, applying Min-Max normalization, and balancing classes using Synthetic Minority Over-sampling Technique (SMOTE).
  • Employed a hybrid deep learning architecture combining 1D Convolutional Neural Networks (CNNs) for feature extraction and transformer layers for temporal pattern analysis, optimized with Adam and regularization techniques.

Main Results:

  • Achieved an exceptional accuracy, precision, and F1-score of 99.99% across all five arrhythmia classes.
  • Demonstrated superior performance compared to the TN4 model and other leading arrhythmia detection models.
  • Highlighted the robustness of features extracted by the CNNs and the hybrid deep architecture.

Conclusions:

  • The developed deep learning model demonstrates high efficacy and potential for real-time, scalable arrhythmia detection.
  • This AI-driven approach can significantly contribute to advancing personalized digital healthcare solutions for cardiovascular conditions.
  • The model's performance suggests a promising tool for early diagnosis and management of arrhythmias, potentially reducing mortality rates.