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Open AccessArticle

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

1
Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
2
Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
3
Department of Electronic Engineering, Universidad de San Buenaventura, Cali 760033, Colombia
4
IRCCS Istituti Clinici Scientifici Maugeri, 20138 Milan, Italy
5
Department of Internal Medicine, IRCCS Humanitas Clinical and Research Center, Humanitas University, 20089 Rozzano, Italy
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(7), 724; https://doi.org/10.3390/e22070724
Received: 27 May 2020 / Revised: 26 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020
(This article belongs to the Collection Feature Papers in Information Theory)
It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.
Keywords: conditional entropy; information dynamics; time series analysis; heart rate variability; systolic blood pressure; cardiovascular control; autonomic nervous system; head-up tilt; Parkinson disease conditional entropy; information dynamics; time series analysis; heart rate variability; systolic blood pressure; cardiovascular control; autonomic nervous system; head-up tilt; Parkinson disease
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MDPI and ACS Style

Porta, A.; Valencia, J.F.; Cairo, B.; Bari, V.; De Maria, B.; Gelpi, F.; Barbic, F.; Furlan, R. Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy? Entropy 2020, 22, 724.

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