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

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance