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Related Concept Videos

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

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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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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

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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.
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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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Dysrhythmias VI: Management of Dysrhythmias01:25

Dysrhythmias VI: Management of Dysrhythmias

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Dysrhythmia management involves a multifaceted approach, incorporating pharmacological treatments, medical procedures, surgical interventions, lifestyle modifications, and patient education.Pharmacological ManagementAntiarrhythmic Drugs:Class I (Sodium Channel Blockers): This class includes quinidine and procainamide, which reduce the speed of impulse conduction in the heart, stabilize the cardiac membrane, and control arrhythmias. Quinidine and procainamide are Class IA agents that prolong the...
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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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Related Experiment Video

Updated: Aug 5, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification.

Zeynep Ozpolat1, Murat Karabatak1

  • 1Department of Software Engineering, Firat University, 23119 Elazig, Turkey.

Diagnostics (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study explored quantum machine learning for heart rhythm classification using electrocardiograms (ECG). While classical SVM showed higher accuracy, quantum SVM demonstrated promising performance for medical signal analysis despite current limitations.

Keywords:
electrocardiography classificationmachine learningquantum computingquantum support vector machine

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Area of Science:

  • Medical technology
  • Quantum computing
  • Machine learning

Background:

  • Electrocardiograms (ECG) are crucial for diagnosing heart diseases like arrhythmia and heart failure.
  • Expert interpretation of ECGs can be time-consuming and subjective.
  • Computer-assisted methods, particularly machine learning, offer potential for automated ECG analysis.

Purpose of the Study:

  • To investigate the application of a quantum-based machine learning algorithm for classifying heart rhythms from ECG data.
  • To compare the performance of a quantum support vector machine (QSVM) against a classical support vector machine (SVM).

Main Methods:

  • ECG signal properties were transformed into a qubit structure using Principal Component Analysis (PCA).
  • The quantum support vector machine (QSVM) algorithm was employed for classification.
  • Quantum computer simulations using Qiskit were utilized for experimental analysis and comparisons with classical SVM.

Main Results:

  • Classical SVM achieved an accuracy of 86.96%.
  • Quantum SVM (QSVM) achieved an accuracy of 84.64%.
  • Both methods demonstrated successful performance, even with limitations on dataset size and qubit numbers.

Conclusions:

  • Quantum-based machine learning frameworks show potential for analyzing medical signal data, including ECGs.
  • Despite current resource constraints in quantum computing, QSVM exhibits viable performance for medical applications.
  • This study contributes to the advancement of quantum machine learning in medical signal processing.