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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
Special considerations while measuring oxygen saturation01:19

Special considerations while measuring oxygen saturation

Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
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Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Pulse rhythm01:30

Pulse rhythm

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 muscle...

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Updated: Jun 27, 2026

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Evaluation of Bubble Entropy Using Heart Rate Variability.

Dimitrios Platakis1, Roberto Sassi2, George Manis1

  • 1Department of Computer Science and Engineering, University of Ioannina, 45500 Ioannina, Greece.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Bubble entropy, a new entropy definition, effectively classifies cardiac RR time series. It outperforms Sample entropy, Approximate entropy, and Permutation entropy in accuracy and feature ranking for biomedical engineering applications.

Keywords:
approximate entropybubble entropyentropypermutation entropysample entropy

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Area of Science:

  • Biomedical Engineering
  • Complex Systems Analysis
  • Information Theory

Background:

  • Entropy measures are crucial for analyzing physiological time series, particularly RR intervals in electrocardiography.
  • Existing entropy measures like Sample Entropy, Approximate Entropy, and Permutation Entropy have limitations in capturing complex signal dynamics.
  • Bubble entropy offers a novel approach with a physical interpretation based on ordering vectors in an embedding space.

Purpose of the Study:

  • To evaluate the efficacy of Bubble entropy in classifying cardiac RR time series.
  • To compare Bubble entropy's performance against established entropy measures (Sample Entropy, Approximate Entropy, Permutation Entropy).
  • To assess the utility of Bubble entropy as a feature for machine learning-based cardiac patient classification.

Main Methods:

  • RR time series data from healthy individuals and cardiac patients were analyzed.
  • Bubble entropy was calculated and compared with Sample entropy, Approximate entropy, and Permutation entropy.
  • Machine learning classifiers (k-NN, SVM, Logistic Regression, Gaussian Naive Bayes) were employed for classification tasks.
  • Feature evaluation methods were used to assess the discriminative power of each entropy measure.

Main Results:

  • Bubble entropy demonstrated superior classification accuracy compared to Sample entropy, Approximate entropy, and Permutation entropy.
  • Feature ranking analysis indicated that Bubble entropy provides more effective features for distinguishing between healthy and patient groups.
  • The proposed Bubble entropy metric showed robust performance across different machine learning models.

Conclusions:

  • Bubble entropy is a promising new metric for the analysis of biomedical time series, particularly RR intervals.
  • Its ability to accurately classify cardiac conditions suggests potential clinical applications in cardiology.
  • Bubble entropy offers advantages over traditional entropy measures in terms of both discriminatory power and feature interpretability.