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Related Concept Videos

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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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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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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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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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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ECG-based heartbeat classification for arrhythmia detection: A survey.

Eduardo José da S Luz1, William Robson Schwartz2, Guillermo Cámara-Chávez1

  • 1Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil.

Computer Methods and Programs in Biomedicine
|January 18, 2016
PubMed
Summary

This review surveys automated electrocardiogram (ECG) methods for classifying heart abnormalities. It details signal processing, feature extraction, and machine learning techniques for improved cardiac diagnostics.

Keywords:
ECG-based signal processingFeature extractionHeartbeat classificationHeartbeat segmentationLearning algorithmsPreprocessing

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electrocardiogram (ECG) is a non-invasive tool for assessing heart electrical activity.
  • Analyzing ECG waveforms aids in detecting cardiac abnormalities.
  • Automated heartbeat classification methods have advanced significantly.

Purpose of the Study:

  • To provide a comprehensive survey of state-of-the-art automated ECG-based heartbeat classification methods.
  • To detail preprocessing, segmentation, feature extraction, and learning algorithms.
  • To discuss evaluation databases and propose a future research workflow.

Main Methods:

  • Literature review of automated ECG-based heartbeat classification techniques.
  • Analysis of ECG signal preprocessing and heartbeat segmentation.
  • Examination of feature description methods and machine learning algorithms.
  • Discussion of AAMI-standardized databases (ANSI/AAMI EC57:1998/(R)2008).

Main Results:

  • Identification of key components in automated ECG analysis: preprocessing, segmentation, feature extraction, and classification.
  • Overview of common algorithms and evaluation methodologies.
  • Highlighting of limitations and challenges in current literature.

Conclusions:

  • Automated ECG analysis is crucial for cardiac diagnostics.
  • Standardized evaluation using databases like AAMI is essential.
  • Future work should address current limitations and refine evaluation processes.