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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

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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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ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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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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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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Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

963
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...
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Pulse rhythm01:30

Pulse rhythm

807
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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
807
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

602
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...
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Updated: Jul 7, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation.

Nitish Katal1, Saurav Gupta1, Pankaj Verma2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning accurately detects heart arrhythmia from electrocardiogram (ECG) signals. This study compares a small CNN against GoogLeNet, showing promising results for early disease detection and intervention.

Keywords:
ECGarrhythmia detectiondeep learninghealthcaremachine learning

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Heart disease is a leading global cause of mortality.
  • Arrhythmia presents a significant health risk, necessitating early detection.
  • Electrocardiogram (ECG) signals are crucial for identifying cardiac irregularities.

Purpose of the Study:

  • To investigate deep learning methods for automated arrhythmia identification from ECG data.
  • To evaluate the performance of a novel small Convolutional Neural Network (CNN).
  • To compare the proposed CNN against established pretrained models like GoogLeNet.

Main Methods:

  • Utilized deep learning techniques for pattern recognition in ECG signals.
  • Developed and trained a small Convolutional Neural Network (CNN).
  • Benchmarked the CNN's performance against GoogLeNet using metrics like accuracy, specificity, precision, and F1 score.

Main Results:

  • Deep learning models demonstrated high efficacy in identifying arrhythmia from ECG.
  • The proposed small CNN achieved competitive performance.
  • Comparative analysis highlighted the potential of deep learning for arrhythmia detection.

Conclusions:

  • Deep learning-based approaches show significant promise for accurate arrhythmia identification using ECG.
  • Early and precise detection of arrhythmia can lead to timely medical intervention.
  • The developed CNN offers a viable tool for clinical application in cardiovascular health.