Automatic Sleep Stage Detection using a Single Channel Frontal EEG

Sleep stage detection algorithms can significantly reduce the workload of manual sleep staging and in improving sleep disorder diagnostics. In this paper, we focus on the automatic detection of sleep stages from a frontal channel EEG using expert defined features in both time and frequency domain, fed to a random forest classifier. The proposed approach shows that using a single frontal channel EEG signal as input to automated sleep scoring algorithms is as effective as using EEGs recorded from the central and occipital regions. Mean overall accuracy, precision and recall were respectively of 72.98%, 79.75% and 71.83%, when validating our method on the MGH (Massachusetts General Hospital), You snooze, you win dataset.