Performance analysis of hurst´s exponent estimators in higly immature breathing patterns of preterm infants

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.