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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

149
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...
149
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

392
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,...
392
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

132
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...
132
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

115
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...
115
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

855
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
855
Electrocardiogram01:29

Electrocardiogram

3.1K
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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Related Experiment Video

Updated: Sep 1, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

Atta-Ur Rahman1, Rizwana Naz Asif2, Kiran Sultan3

  • 1Department of Computer Science, College of Computer Science, and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam 31441, Saudi Arabia.

Computational Intelligence and Neuroscience
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

Transfer learning methods accurately detect ECG arrhythmia. Deep learning models like AlexNet, SqueezeNet, and ResNet50 achieved high accuracy, improving upon previous machine learning techniques for heart condition diagnosis.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Heart disease is a rapidly growing global health concern, particularly for individuals over 40.
  • Accurate and efficient detection of heart abnormalities is crucial for timely diagnosis and treatment.
  • Traditional machine learning methods for ECG analysis have limitations in accuracy and time management.

Purpose of the Study:

  • To compare the accuracy of different transfer learning methods for Electrocardiogram (ECG) classification.
  • To evaluate the effectiveness of deep learning models in detecting ECG Arrhythmia.
  • To introduce a novel model, CAA-TL, for enhanced ECG analysis.

Main Methods:

  • Utilized a multiclassification approach for ECG datasets, including Kaggle and the MIT-BIH dataset.
  • Applied and compared deep learning models: ResNet50, AlexNet, and SqueezeNet.
  • Integrated real-time, augmented, and fused datasets for robust model training.

Main Results:

  • All tested deep learning methods demonstrated significant accuracy improvements over prior research.
  • AlexNet achieved 98.8% accuracy, SqueezeNet 90.08%, and ResNet50 91% in ECG arrhythmia detection.
  • The study highlighted the potential of transfer learning for precise and efficient ECG analysis.

Conclusions:

  • Transfer learning significantly enhances the accuracy and efficiency of ECG arrhythmia detection.
  • Deep learning models offer a promising advancement over traditional machine learning in cardiovascular diagnostics.
  • Further research into deep learning approaches can refine ECG analysis for better patient outcomes.