Dataset related to article: "Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?"

This record contains data related to article "Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?"

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.