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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

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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Electrocardiogram Fundamentals01:28

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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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ECG Interpretation of Rhythms01:24

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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.
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Instrumentation Amplifier01:25

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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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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
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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The Effect of Segmentation Variability in Forward ECG Simulation.

Beata Ondrusova1,2, Machteld Boonstra3, Jana Svehlikova1

  • 1Institute of Measurement Science, SAS, Bratislava, Slovakia.

Computing in Cardiology
|October 6, 2023
PubMed
Summary
This summary is machine-generated.

Heart segmentation variability significantly impacts electrocardiographic imaging (ECGI) simulations. Even small shape changes can alter body surface potential (BSP) calculations, affecting ECG imaging accuracy.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Cardiology

Background:

  • Patient-specific anatomical models are crucial for Electrocardiographic imaging (ECGI).
  • The influence of variations in heart segmentation on ECGI outcomes has not been previously investigated.
  • Understanding segmentation variability is key to improving the reliability of ECGI.

Purpose of the Study:

  • To evaluate the impact of heart segmentation variability on Electrocardiographic imaging (ECGI) simulations.
  • To quantify how anatomical model variations affect the accuracy of simulated body surface potentials (BSPs).
  • To determine the sensitivity of ECG simulation results to cardiac shape uncertainty.

Main Methods:

  • A statistical shape model was created from multiple segmentations of a single patient's heart.
  • 262 distinct cardiac geometries were generated from this model.
  • Electrocardiographic imaging (ECGI) forward computation was performed using an equivalent dipole layer cardiac source model and five ventricular stimulation protocols to simulate body surface potentials (BSPs).

Main Results:

  • Variability in simulated BSPs was assessed using Pearson's correlation coefficient (CC) against a mean cardiac shape model.
  • Apical pacing showed the lowest BSP variability (average CC = 0.98 ± 0.03), while right ventricular free wall pacing exhibited the highest (average CC = 0.90 ± 0.23).
  • Low amplitude BSPs demonstrated greater QRS morphology variation compared to high amplitude signals, indicating shape uncertainty's significant effect.

Conclusions:

  • Cardiac shape uncertainty, stemming from segmentation variability, significantly influences Electrocardiographic imaging (ECGI) results.
  • The degree of impact varies depending on the cardiac pacing location and the amplitude of the simulated signals.
  • These findings highlight the need to account for segmentation variability in ECGI to ensure accurate patient-specific cardiac electrical activity reconstruction.