Inhomogeneous point-process entropy: An instantaneous measure of complexity in discrete systems
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PhysRevE.89.052803.pdf
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1.23 MB
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Author(s)
Valenza, Gaetano
Citi, Luca
Scilingo, Enzo Pasquale
Barbieri, Riccardo
Date Issued
May 2014
Journal
Physical Review E
Publisher
American Physical Society
Citation
Valenza, Gaetano, Luca Citi, Enzo Pasquale Scilingo, and Riccardo Barbieri. “Inhomogeneous Point-Process Entropy: An Instantaneous Measure of Complexity in Discrete Systems.” Phys. Rev. E 89, no. 5 (May 2014). © 2014 American Physical Society
Version
Final published version
Abstract
Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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DOI of Published Version
https://doi.org/10.1103/PhysRevE.89.052803
