Bioradiolocation-Based Multi-Class Sleep Stage Classification Using Time and Frequency Features with Random Forest Classifier

Sleep disorders are a common problem that disrupts our regular sleeping patterns. To diagnose sleep disorders, Long-term monitoring of sleep could be useful. In this paper an automated scheme of sleep staging is presented based on Bioradiolocation signals using time and frequency domain feature extraction and Random Forest Classifier. This experiment is validated using data of 32 subjects without sleep-related breathing disorders. A Random Forest based algorithm is used for two, three, four and five-stage classification. We achieved the best performance so far (89.35% accuracy and 0.65 Cohens kappa) on 2-stage, 75.3% accuracy on 3-stage, 56.18% on 4-stage, and 54.2% accuracy on 5-stage classification with BRL Signals. These results show high potential in real-life sleep stage monitoring systems.