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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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A Two-Steps-Ahead Estimator for Bubble Entropy.

George Manis¹, Matteo Bodini², Massimo W Rivolta²

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

Entropy (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

Bubble entropy (bEn) analysis was advanced with a new two-steps-ahead estimator, showing improved statistical significance and regular behavior in heart rate variability (HRV) data. This research refines understanding of bEn for physiological signal analysis.

Keywords:

bubble entropy entropy limited dependence on parameters

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Area of Science:

Complexity and Information Theory
Biomedical Signal Processing

Background:

Bubble entropy (bEn) is an entropy metric with limited parameter dependence.
bEn assesses changes in the ordering of samples, not direct conditional entropy.
Autoregressive (AR) process analysis revealed differing bEn relations for odd/even sample lengths (m).

Purpose of the Study:

Investigate a two-steps-ahead estimator for bubble entropy.
Compare the new estimator with the original bEn on simulated and real-world data.
Explore a novel normalization factor for bEn, ensuring a value of 1 for white Gaussian noise (WGN).

Main Methods:

Theoretical analysis of expected values for AR processes.
Comparison of original and two-steps-ahead bEn estimators on simulated time series.
Application and evaluation of estimators on heart rate variability (HRV) data.

Main Results:

Both bEn estimators demonstrated comparable discriminating power.
The two-steps-ahead estimator exhibited slightly higher statistical significance and regularity for 10
Minimal dependence on the parameter m was observed for the new estimator.

Conclusions:

The study enhances the understanding of bubble entropy, particularly for HRV analysis.
The two-steps-ahead estimator offers improved performance characteristics.
Further insights into the definition and application of bEn estimators were gained.