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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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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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.
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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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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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Cardiopulmonary Resuscitation III: AED Use

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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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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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Related Experiment Video

Updated: Aug 30, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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A two-step method for paroxysmal atrial fibrillation event detection based on machine learning.

Ya'nan Wang1, Sen Liu1, Haijun Jia1

  • 1Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Mathematical Biosciences and Engineering : MBE
|August 29, 2022
PubMed
Summary

This study introduces a two-step machine learning method for accurately detecting paroxysmal atrial fibrillation (AFp) events in long-term ECGs. The approach precisely identifies AFp start and end points, improving diagnosis and patient management.

Keywords:
RR intervalsatrial fibrillation event detectionmachine learningphased trainingtwo-step method

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Atrial fibrillation (AF) detection is crucial for timely clinical intervention.
  • Current algorithms often neglect the precise start and end points of paroxysmal AF (AFp) episodes.
  • Accurate AFp event identification in long-term electrocardiograms (ECGs) remains a challenge.

Purpose of the Study:

  • To propose a novel two-step machine learning method for accurate AFp event detection.
  • To precisely locate the start and end points of AFp episodes within long-term ECG data.
  • To enhance the assessment of AF burden index for AFp patients.

Main Methods:

  • A two-step machine learning approach utilizing R-to-R intervals.
  • Step 1: Support Vector Machine (SVM) classifies ECG rhythms into AFp, persistent AF (AFf), and non-AF (N).
  • Step 2: Deep Convolutional Neural Network (CNN) with phased training identifies AF beats to pinpoint AFp episode start and end times.

Main Results:

  • The proposed method achieved a final score U of 1.9310 on the 4th China Physiological Signal Challenge 2021 test set.
  • Demonstrated superior performance in detecting AFp events compared to existing algorithms.
  • Successfully identified the beginning and end of AF episodes within AFp rhythms.

Conclusions:

  • The developed two-step machine learning method offers an effective solution for accurate AFp detection.
  • Precise localization of AFp events aids in better clinical diagnosis and intervention strategies.
  • This technique is valuable for calculating the AF burden index in AFp patients.