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Related Concept Videos

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

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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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Instrumentation Amplifier01:25

Instrumentation Amplifier

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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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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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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Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

Juliana Carneiro Gomes1, Maíra Araújo de Santana1, Aras Ismael Masood2

  • 1Polytechnique School of the University of Pernambuco, Recife, Brazil.

Medical & Biological Engineering & Computing
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

COVID-19 significantly impacts cardiac function, affecting electrocardiography (ECG) signals. Machine learning analysis of ECGs can accurately support COVID-19 diagnosis, distinguishing it from heart conditions.

Keywords:
COVID-19 clinical diagnosisCOVID-19 computer-aided diagnosisDeep learningElectrocardiographyHybrid deep architectures

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Area of Science:

  • Cardiology
  • Computational Biology
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, initially perceived as a respiratory illness, has demonstrated significant effects on cardiac function and hematological parameters.
  • Understanding the cardiac implications of COVID-19 is crucial for comprehensive patient management and diagnosis, especially in emergency care settings.

Purpose of the Study:

  • To investigate the impact of COVID-19 on cardiac function through the analysis of electrocardiography (ECG) signals.
  • To develop and evaluate a machine learning system for supporting the clinical diagnosis of COVID-19 using automated ECG analysis.

Main Methods:

  • Utilized a public database of ECG signals from emergency care, including those from COVID-19 patients, individuals with heart conditions, and healthy controls.
  • Proposed a hybrid deep learning architecture combining pre-trained Convolutional Neural Networks (CNNs) (LeNet, ResNet, VGG16) for feature extraction and Random Forests for classification.
  • Employed particle swarm optimization for attribute selection, reducing feature dimensionality and enhancing model efficiency.

Main Results:

  • The hybrid VGG16-Random Forest model achieved high diagnostic accuracy (94%) and a kappa index of 0.91.
  • Achieved excellent performance metrics, including 100% sensitivity, 100% specificity, and 100% area under the ROC curve.
  • Demonstrated that COVID-19 has a considerable and distinct influence on cardiac function, without confusion with other heart diseases or healthy ECG patterns.

Conclusions:

  • COVID-19 significantly affects cardiac function, and this impact is detectable through ECG analysis.
  • A non-invasive, technologically scalable solution using hybrid deep learning architectures can effectively support the clinical diagnosis of COVID-19 in emergency settings.
  • Automated ECG analysis presents a promising tool for rapid and accurate COVID-19 diagnosis, aiding clinical decision-making.