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

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 Henonmap-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 l(1)-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. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

The assessment of dynamical complexity is vital in various fields such as medical diagnostics, mechanical system fault analysis, and astrophysics. Traditional entropy measures are limited by short data length and sensitive predetermined parameters. Distribution entropy (DistEn) is a robust complexity estimator, but it fails to distinguish noise and chaotic signals while underestimating their complexity at higher scales. To overcome these limitations, an improved distribution entropy (ImDistEn) is proposed, which utilizes embedded vectors' orientation, ordinality, and l(1)-norm distance information. Simulation results demonstrate that ImDistEn can accurately assess the complexity of different types of signals.