  Sleep stage classification is a common method used by experts to monitor the
quantity and quality of sleep in humans, but it is a time-consuming and
labour-intensive task with high inter- and intra-observer variability. Using
Wavelets for feature extraction and Random Forest for classification, an
automatic sleep-stage classification method was sought and assessed. The age of
the subjects, as well as the moment of sleep (early-night and late-night), were
confronted to the performance of the classifier. From this study, we observed
that these variables do affect the automatic model performance, improving the
classification of some sleep stages and worsening others.
