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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

1.5K
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...
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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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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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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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Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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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...
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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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Detection of arrhythmia using weightage-based supervised learning system for COVID-19.

Yashodhan Ketkar1, Sushopti Gawade2

  • 1Department of Information Technology Engineering, Pillai College of Engineering, Panvel, Maharashtra 410206, India.

Intelligent Systems with Applications
|June 6, 2025
PubMed
Summary

This study introduces an automated method using supervised learning to detect cardiovascular issues from ECG signals in COVID-19 patients. The system achieved high accuracy (97%) in identifying arrhythmias, aiding in disease prognosis.

Keywords:
Arrhythmia detectionAutomated model generationAutomated model trainingMachine learning in healthcareSupervised learning algorithmsWeightage based model selection,

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Area of Science:

  • Cardiology
  • Infectious Diseases
  • Artificial Intelligence in Medicine

Background:

  • COVID-19 infection can cause severe cardiovascular complications, leading to mortality.
  • Cardiovascular problems are a primary cause of death in COVID-19 patients and are key prognostic indicators.
  • Detecting cardiac abnormalities like arrhythmia from electrocardiogram (ECG) signals is crucial for assessing cardiovascular health.

Purpose of the Study:

  • To develop an automated system for identifying cardiovascular abnormalities from ECG signals in COVID-19 patients.
  • To select the most suitable supervised learning model for accurate arrhythmia detection based on user-defined requirements.
  • To improve the efficiency and accuracy of cardiovascular disorder detection in the context of COVID-19.

Main Methods:

  • Utilized supervised learning algorithms for the analysis of ECG signals.
  • Developed a model selection system that assigns weights based on user requirements to identify the optimal predictive model.
  • Trained and tested various models to identify abnormalities in ECG waves indicative of cardiovascular issues.

Main Results:

  • The automated system successfully identified abnormalities in ECG waves.
  • The selected models met user-defined requirements, demonstrating high performance.
  • Achieved up to 97% accuracy and 97% precision in predictive tasks for arrhythmia detection.

Conclusions:

  • The proposed automated method effectively detects cardiovascular abnormalities from ECG signals in COVID-19 patients.
  • The model selection system ensures the deployment of high-performing models tailored to specific needs.
  • This approach offers a promising tool for early detection and improved prognosis of cardiovascular complications associated with COVID-19.