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This chapter describes methodological frameworks for estimating physiological noise in monovariate signals. These approaches are model-free, meaning they do not rely on prior knowledge of the analytical equations governing the underlying physiological processes. The theoretical foundation of the methods is outlined, with a particular focus on the behavior of entropy-based quantifiers under stochastic perturbations. An associated algorithm for the numerical estimation of physiological noise is also provided. The chapter further addresses the application of the methods to signals with time-varying or nonstationary stochastic components, demonstrating its adaptability to realistic physiological scenarios. The chapter concludes with a brief discussion on the potential for reducing dynamical noise in physiological systems. A simple reduction technique is introduced.

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Model-Free Approaches for Dynamical Noise Estimation and Reduction

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  • 发表日期：

    2025年

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  • Andrea Scarciglia,
  • Claudio Bonanno,
  • Gaetano Valenza

摘要

This chapter describes methodological frameworks for estimating physiological noise in monovariate signals. These approaches are model-free, meaning they do not rely on prior knowledge of the analytical equations governing the underlying physiological processes. The theoretical foundation of the methods is outlined, with a particular focus on the behavior of entropy-based quantifiers under stochastic perturbations. An associated algorithm for the numerical estimation of physiological noise is also provided. The chapter further addresses the application of the methods to signals with time-varying or nonstationary stochastic components, demonstrating its adaptability to realistic physiological scenarios. The chapter concludes with a brief discussion on the potential for reducing dynamical noise in physiological systems. A simple reduction technique is introduced.