INTRODUCTION: Sleep stage classification is an important task for the timely diagnosis of sleep-related disorders, which are one the most common indicator of illness.
OBJECTIVE: An automated sleep scoring implementation with promising generalization capabilities is presented, aiding towards eliminating the tedious procedure of manual sleep scoring.
METHODS:Two Electroencephalogram (EEG) channels and the Electrooculogram (EOG) channel are utilized as inputs for feature extraction both in the time and frequency domain, while temporal feature changes are utilized in order to capture contextual information of the signals. An ensemble tree-based and a neural network approach are presented at the classification process.
RESULTS: A total of 66 subjects belonging to three different groups (healthy, placebo, drug intake) were included in the study. The tree-based classification method outperforms the neural network at all cases.
CONCLUSION: State of the art results are achieved, while it is highlighted that using jointly the healthy and patient subjects dataset, boosts the model’s accuracy and generalization capability.