Measuring the Complexity of a Physiological Time Series: a Review
Research background and hypothesis. Complex Systems Theory indeed is a solid basis for a scientific approach
in the analysis of living, learning, and evolving systems. A number of different entropy estimators have been applied
to physiological time series attempting to quantify its complexity.
Research aim. The aim of the paper is to review most popular complexity estimators (entropies) applied in
biological, medical, sport and exercise sciences and their performances.
Research results. Various measures of complexity were developed by scientists to compare time series and
distinguish regular (e. g. periodic), chaotic, and random behavior. In this paper a brief review of most popular
complexity estimators – Sample Entropy, Control Entropy, Spectral Entropy, Wavelet Entropy, Singular-Value
Decomposition Entropy, Permutation Entropy, Base-Scale Entropy, Entropy based on Lempel-Ziv algorithm – and
their performances is presented. In biological applications they are used to distinguish peculiarities in behavior of
biological systems or may serve as non-invasive, objective means of determining physiological changes under steady
or non-steady state conditions.
Discussion and conclusions. The choice of a particular entropy estimator is determined by the goal type, the
capability of estimators in characterizing the constraints on a physiological time series, its robustness to noise
considering the above-mentioned advantages and disadvantages of particular algorithms. It is difficult to apply
analytical solutions in the analysis of behavior of living, learning, and evolving systems and new approaches and
solutions remain on the agenda.
Keywords: physiological time series, complexity, entropy.
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