Several Heart Rate Variability (HRV) based novel methodologies for describing heart rate dynamics have been proposed in the literature with the aim of risk assessment. One such methodology is ARFIMA-EGARCH modeling which allows the quantification of long range dependence and time-varying volatility with the aim of describing non-linear and complex characteristics of HRV. This study applies the ARFIMA-EGARCH modeling of HRV recordings from 30 patients of the Noltisalis database to investigate the discrimination power of a set of features comprising currently used linear HRV features (low and high frequency components) and new measures obtained from the modeling such as, long memory in the mean, and persistence and asymmetry in volatility. A subset of the multidimensional HRV features is selected in a two-step procedure using Principal Components Analysis (PCA). Additionally, supervised classification by quadratic discriminant analysis achieves 93.3% of discrimination accuracy between the groups using the new feature set created by PCA.