Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
This work addresses the need for more reliable sleep monitoring tools, but it is incremental as it applies existing methods to analyze demographic and temporal factors.
The study tackled the problem of automating sleep-stage classification to reduce labor and variability, finding that age and sleep timing affect model performance, improving some stages and worsening others.
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.