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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
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Related Experiment Video

Updated: Aug 12, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Automatic COVID-19 severity assessment from HRV.

Cosimo Aliani1, Eva Rossi2, Marco Luchini3

  • 1Department of Information Engineering, University of Florence, Florence, Italy. cosimo.aliani@unifi.it.

Scientific Reports
|January 31, 2023
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This study shows that Random Forest and Support Vector Machine classifiers can accurately detect COVID-19 presence and severity using Heart Rate Variability (HRV) from PPG signals. The method offers a fast, low-cost approach for COVID-19 screening.

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Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology
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Area of Science:

  • Biomedical Engineering
  • Data Science in Medicine
  • Cardiology

Background:

  • COVID-19 can cause microvascular disease due to inflammation and blood coagulation.
  • Assessing COVID-19 presence and severity often requires complex diagnostic methods.

Purpose of the Study:

  • To develop and validate a methodological approach for assessing COVID-19 presence and severity.
  • To utilize machine learning classifiers on Heart Rate Variability (HRV) parameters from photoplethysmographic (PPG) signals.

Main Methods:

  • Employed Random Forest (RF) and Support Vector Machine (SVM) supervised classifiers.
  • Extracted HRV parameters from PPG signals of 50 healthy and 93 COVID-19 subjects (mild/moderate severity).
  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection.

Main Results:

  • Both RF and SVM classifiers demonstrated high accuracy in distinguishing between healthy individuals and COVID-19 patients.
  • The RF classifier achieved 94% accuracy in differentiating healthy subjects from those with mild COVID-19.
  • The classifiers effectively differentiated between mild and moderate COVID-19 severity.

Conclusions:

  • RF and SVM classifiers show strong capability in detecting COVID-19 and its severity from HRV parameters.
  • The proposed method offers a potentially low-cost and fast screening tool for COVID-19.
  • This approach serves as a promising foundation for future COVID-19 screening research.