An improved multiscale distribution entropy for analyzing complexity of real-world signals

Bhabesh Deka and Dipen Deka

Chaos, Solitons & Fractals, 2022, vol. 158, issue C

Abstract: Assessment of the dynamical complexity of signals or systems is very crucial in medical diagnostics, fault analysis of mechanical systems, astrophysics and many more. Although there have been tremendous improvements in entropy measures as complexity estimator, most of these measures are affected by short data length and are highly sensitive to predetermined parameters. These issues are addressed quite successfully by distribution entropy (DistEn), a robust estimator of complexity for many signals. However, it fails to discriminate random noise, pink noise and Henon map-based chaotic signals. Furthermore, it underestimates the complexity of chaotic signals at higher scales. To circumvent these problems, we propose an improved distribution entropy (ImDistEn), which utilizes embedded vectors' orientation, ordinality and ℓ1-norm distance information for its computation. Simulation results show that ImDistEn can provide clear distinction of different classes of real-world signals, besides accurately assessing the complexity of various synthetic signals.

Keywords: Dynamical complexity assessment; Distribution entropy; Multiscale entropy; Heart rate variability (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922003113

DOI: 10.1016/j.chaos.2022.112101

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