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Related Concept Videos

Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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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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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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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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Related Experiment Video

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.

Getu Tadele Taye1, Eun Bo Shim2, Han-Jeong Hwang1

  • 1Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea.

Frontiers in Physiology
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

Predicting ventricular fibrillation (VF) early is crucial for patient survival. Analyzing QRS complex shape features significantly improves prediction accuracy to 98.6%, outperforming traditional heart rate variability (HRV) methods.

Keywords:
QRS complex shapeQRS complex singed areaR-peak amplitudeprediction accuracyventricular fibrillationventricular tachyarrhythmiaventricular tachycardia

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Ventricular tachyarrhythmia (VTA), encompassing ventricular tachycardia (VT) and ventricular fibrillation (VF), poses a life-threatening risk.
  • Existing prediction models using traditional heart rate variability (HRV) features show promise but require enhanced accuracy for critical conditions like VF.
  • Early detection of VF is vital for timely intervention, particularly for patients with implantable cardiac defibrillators (ICDs).

Purpose of the Study:

  • To investigate the efficacy of QRS complex shape features in improving the early prediction of ventricular fibrillation (VF) onset.
  • To compare the predictive performance of QRS complex shape features against traditional HRV features for VF detection.
  • To assess the potential of enhanced prediction models for guiding defibrillation timing in ICDs.

Main Methods:

  • Extraction of features from 120-second electrocardiogram (ECG) and HRV signals, including QRS complex signed area and R-peak amplitude.
  • Development and validation of two artificial neural network (ANN) classifiers using distinct feature sets: traditional HRV and QRS complex shape.
  • Implementation of a 10-fold cross-validation strategy for robust model evaluation.

Main Results:

  • Traditional HRV features achieved a prediction accuracy of 72% for VF onset 30 seconds prior.
  • QRS complex shape features demonstrated a significantly higher prediction accuracy of 98.6% for VF onset.
  • The study highlights the substantial contribution of QRS complex morphology to predictive model performance.

Conclusions:

  • QRS complex shape analysis offers a significant advancement in the early prediction of ventricular fibrillation (VF).
  • The superior accuracy of QRS features suggests their critical role in developing more effective VF detection algorithms.
  • These findings have direct implications for optimizing defibrillation strategies in implantable cardiac devices.