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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

852
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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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

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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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Using Machine Learning Algorithms to Determine the Post-COVID State of a Person by Their Rhythmogram.

Sergey V Stasenko1, Andrey V Kovalchuk2, Evgeny V Eremin3

  • 1Neurotechnology Department, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia.

Sensors (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to detect post-COVID conditions using electrocardiogram (ECG) data. This technique identifies "cardiospikes," potentially marking COVID-19

Keywords:
COVID-19data analysiselectrocardiogrammachine learning algorithmspost-COVID state

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Area of Science:

  • Cardiology
  • Medical Technology
  • Infectious Diseases

Background:

  • Post-COVID conditions affect cardiac function, necessitating reliable detection methods.
  • Electrocardiogram (ECG) data offers a non-invasive window into heart rhythm regulation.
  • Current diagnostic tools may not fully capture the nuances of post-COVID cardiac changes.

Purpose of the Study:

  • To introduce a novel method for detecting post-COVID conditions using ECG.
  • To identify specific ECG markers, termed "cardiospikes," indicative of a past COVID-19 infection.
  • To explore the potential of these cardiospikes as objective markers for COVID-specific heart rhythm regulation.

Main Methods:

  • Utilized a convolutional neural network (CNN) to analyze ECG data.
  • Developed a detection algorithm for identifying "cardiospikes" in post-COVID patients.
  • Conducted blood parameter measurements and created profiles for recovered COVID-19 patients.

Main Results:

  • Achieved 87% accuracy in detecting cardiospikes within a test sample.
  • Confirmed that cardiospikes are inherent physiological signals, not hardware artifacts.
  • Established correlations between ECG findings and blood parameter profiles in recovered patients.

Conclusions:

  • The novel CNN-based method effectively detects cardiospikes, indicating post-COVID cardiac changes.
  • Cardiospikes show promise as reliable biomarkers for COVID-19's impact on heart rhythm.
  • Findings support the development of remote screening tools for COVID-19 diagnosis and monitoring.