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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.3K
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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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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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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Instrumentation Amplifier01:25

Instrumentation Amplifier

732
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...
732
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

184
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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Related Experiment Video

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Automatic ECG classification and label quality in training data.

Ľubomír Antoni1, Erik Bruoth1, Peter Bugata2

  • 1Institute of Computer Science, Faculty of Science, Pavol Jozef Šafárik University in Košice, Jesenná 5, 040 01, Košice, Slovakia.

Physiological Measurement
|April 22, 2022
PubMed
Summary
This summary is machine-generated.

A machine learning algorithm using a deep convolutional neural network was developed to detect cardiac abnormalities from electrocardiogram (ECG) recordings. The model demonstrated strong performance, outperforming standard 12-lead ECGs with reduced lead configurations.

Keywords:
ECG signaldeep neural networkmulti-label classification

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cardiac abnormalities are often diagnosed using electrocardiogram (ECG) recordings.
  • The diagnostic potential of reduced-lead ECGs compared to standard 12-lead ECGs requires further investigation.
  • Machine learning offers promising avenues for automated ECG analysis.

Purpose of the Study:

  • To design a machine learning algorithm for identifying cardiac abnormalities from ECGs with varying numbers of leads.
  • To assess the diagnostic performance of reduced-lead ECGs against standard 12-lead ECGs.
  • To optimize ECG analysis for clinical applicability and efficiency.

Main Methods:

  • Development of a deep convolutional neural network (1D ResNet50 variant).
  • Pre-training the model on a large dataset with a novel label mapping to SNOMED codes.
  • Fine-tuning the model for the PhysioNet/Computing in Cardiology Challenge 2021 metrics.

Main Results:

  • The proposed approach achieved 5th place in the Challenge (0.52 score).
  • An improved post-Challenge solution was ranked best across 12-lead, 3-lead, and 2-lead configurations (scores 0.62, 0.61, 0.59).
  • Identified specific cardiac labels diagnosable with as few as two ECG leads.

Conclusions:

  • Machine learning models can effectively identify cardiac abnormalities from ECGs.
  • Reduced-lead ECG configurations show significant diagnostic potential, comparable to 12-lead ECGs for certain conditions.
  • Optimized label mapping and model training are crucial for enhancing diagnostic accuracy in automated ECG analysis.