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

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  • Deka, Bhabesh
  • Deka, Dipen

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.

Suggested Citation

  • Deka, Bhabesh & Deka, Dipen, 2022. "An improved multiscale distribution entropy for analyzing complexity of real-world signals," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922003113
    DOI: 10.1016/j.chaos.2022.112101
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    References listed on IDEAS

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    1. Wu, Shuen-De & Wu, Chiu-Wen & Humeau-Heurtier, Anne, 2016. "Refined scale-dependent permutation entropy to analyze systems complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 454-461.
    2. Silva, Antonio Samuel Alves & Menezes, Rômulo Simões Cezar & Rosso, Osvaldo A. & Stosic, Borko & Stosic, Tatijana, 2021. "Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Alves Xavier, Sílvio Fernando & Xavier, Érika Fialho Morais & Jale, Jader Silva & Stosic, Tatijana & Santos, Carlos Antonio Costa dos, 2021. "Multiscale entropy analysis of monthly rainfall time series in Paraíba, Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    4. Wu, Shuen-De & Wu, Chiu-Wen & Lee, Kung-Yen & Lin, Shiou-Gwo, 2013. "Modified multiscale entropy for short-term time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5865-5873.
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    Cited by:

    1. Bukhari, Ayaz Hussain & Raja, Muhammad Asif Zahoor & Alquhayz, Hani & Abdalla, Manal Z.M. & Alhagyan, Mohammed & Gargouri, Ameni & Shoaib, Muhammad, 2023. "Design of intelligent hybrid NAR-GRNN paradigm for fractional order VDP chaotic system in cardiac pacemaker with relaxation oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    2. Zhao, Tong & Li, Zhen & Deng, Yong, 2023. "Information fractal dimension of Random Permutation Set," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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