期刊
ENTROPY
卷 20, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/e20120962
关键词
approximate entropy; sample entropy; range entropy; complexity; self-similarity; Hurst exponent
资金
- National Health and Medical Research Council (NHMRC) of Australia [1091593]
- NHMRC practitioner fellowship [1060312]
- Victorian Government
- Operational Infrastructure Support Grant
- Melbourne Bioinformatics at the University of Melbourne [UOM0042]
- National Health and Medical Research Council of Australia [1060312] Funding Source: NHMRC
Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for temporal complexity analysis of real-world phenomena. However, their relationship with the Hurst exponent as a measure of self-similarity is not widely studied. Additionally, ApEn and SampEn are susceptible to signal amplitude changes. A common practice for addressing this issue is to correct their input signal amplitude by its standard deviation. In this study, we first show, using simulations, that ApEn and SampEn are related to the Hurst exponent in their tolerance r and embedding dimension m parameters. We then propose a modification to ApEn and SampEn called range entropy or RangeEn. We show that RangeEn is more robust to nonstationary signal changes, and it has a more linear relationship with the Hurst exponent, compared to ApEn and SampEn. RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and it has no need for signal amplitude correction. Finally, we demonstrate the clinical usefulness of signal entropy measures for characterisation of epileptic EEG data as a real-world example.
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