Performance analysis of hurst's exponent estimators in higly immature breathing patterns of preterm infants
Xavier Navarro‐Sune, Alain Beuchée, Fabienne Porée, Guy Carrault · 2011
We analyzed the performance of five common estimators of the Hurst parameter (H) in simulated data, i.e. surrogate and synthetic through a fractional Gaussian noise model. Inter-breath signals of 30 preterm infants were used to generate surrogates as well as the synthetic data, which was produced according 3 typical patterns: erratic (EB), periodic (PB) and regular breathing (RB). The discrete wavelet transform was the most efficient for PB, with a concordance correlation coefficient (CCC) of 0.92. The iterative fGn-based estimator was the most efficient for EB and RB, with a CCC of 0.92 and 0.97 respectively, and performed better in the surrogates test with an estimation error of 0.008.