Detection of Sleep Stages (NREM, REM, and Wake) from EEG Signals Using Machine Learning

Abstract

Existing automated sleep stage classification techniques tend to focus on NREM and REM but merrily ignore the Wake phase which is essential for studying insomnia. Our aim is to bring a simple and comprehensible machine learning method to fill this gap of the precise expressions of the Wake stages. Our Gradient Boosting algorithm, compared to other algorithms, has shown 91.08% accuracy in classifying NREM, REM, and the Wake stages from single channel EEG signals. In the meantime, XGBoost demonstrated fantastic performance delivering 90.99% accuracy. To address Wake data scarcity, we integrated SMOTE to enhance overall classifier effectiveness. AdaBoost achieved 81.29% accuracy, but Gradient Boosting did better as it outperformed the baseline by having 89.47% on unseen data. The results presented here help building a clinically intuitive, very accurate instrument for personal sleep monitoring, which is directly applicable to home healthcare and insomnia therapy.