An Undersampled Model for Automated Sleep Stage Scoring Using EEG Data: Utilization of DWT, bagged trees, and random undersampling to achieve more consistent accuracy on the sleepstage problem

Abstract

Sleep is one of the most critical functions of the human body, yet many disorders disrupt this physiological process. These conditions can be diagnosed by observing the pattern and length of sleep stages that a patient enters; however, this process requires the manual scoring of a patient's EEG patterns by a specialist. This process is time-consuming and inaccessible, but the accurate and automated scoring of sleep stages by artificial intelligence would help medical professionals quickly offer diagnoses and treatments. In this paper, we propose a bagged trees model using wavelet decomposition for feature extraction, while also utilizing random undersampling to handle the inherent data imbalance. We achieve 85.1% and 87.1 % accuracy on 5-fold cross validation and the test set, respectively. The accuracy across all stages is consistent, indicating that the model may be more suitable for real-world applications than other models with nominally higher accuracies.