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Bubble Entropy: An Entropy Almost Free of Parameters
George Manis1, Md Aktaruzzaman2, Roberto Sassi2
1Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece.
IEEE Transactions on Bio-Medical Engineering
|February 10, 2017
Summary
We introduce Bubble Entropy, a new method for estimating entropy that minimizes parameter selection. This novel approach offers enhanced stability and descriptive power for analyzing physiological signals like HRV.
Area of Science:
- Complexity Science
- Biomedical Signal Processing
- Information Theory
Background:
- Entropy estimation in physiological signals is crucial for understanding system dynamics.
- Traditional entropy measures often depend heavily on parameter selection, influencing results.
- Parameter dependency can lead to application-specific tuning and subjective interpretations.
Purpose of the Study:
- To propose a novel entropy definition, Bubble Entropy, that reduces parameter dependency.
- To enhance the stability and descriptive power of entropy estimation for physiological data.
- To overcome limitations of existing entropy measures influenced by parameter choices.
Main Methods:
- Bubble Entropy is based on permutation entropy, utilizing the bubble sort algorithm for vector ordering.
- Instead of ranking vectors, it counts the number of swaps performed by bubble sort.
- This creates a coarse-grained distribution for entropy calculation, minimizing parameter influence.
Main Results:
- Bubble Entropy demonstrated remarkable stability across real and synthetic Heart Rate Variability (HRV) signals.
- The new method exhibited superior descriptive and discriminating power compared to established entropy definitions.
- Parameter influence was significantly reduced, with the scale factor completely eliminated.
Conclusions:
- Bubble Entropy offers a robust and almost parameter-free approach to entropy estimation.
- The method's reduced parameter importance (embedding dimension 'm') and eliminated scale factor ('r') improve objectivity.
- This advancement provides a more reliable and less subjective tool for physiological signal analysis.
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