The study aimed to detect stress in newborns by analyzing their heart rate variability. They used a model called mean-reverting fractional Lévy stable motion to estimate the Hurst exponent, which is a key indicator of stress. The researchers found that the speed of reversion parameter in the model is a significant indicator of stress, while the Hurst exponent itself does not provide useful information. By decomposing the Hurst exponent and analyzing its underlying properties, they were able to develop effective diagnostic tools for neonatal stress.