Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder

We have concluded that the selected features that best characterize the underlying process are common to both databases. This supports the fact that the conclusions reached are potentially generalizable. The best results were obtained when the three kinds of features were jointly used. Another notable fact is the small number of features needed to describe the phenomenon. Results suggest that the two first Fbanks, the first CC and the first DFA coefficient are the variables that best describe the RR pattern in OSA and, therefore, are especially relevant to extract discriminative information for apnea screening purposes.