Artifact Detection in Multichannel Sleep EEG using Random Forest Classifier

Detection of artifacts in sleep electroencephalography (EEG) is one of the important tasks on the preprocessing step. Despite many algorithms of artifact detection developed through years, many of them lose their benefits in sleep EEG application. This study proposes a method of artifact detection based on a classification of quasi-stationary EEG epochs with random forest classifier. The method was tested on data of three sleep stages and pre-sleep wake EEG. Results showed 16% increase in F1 for the wake and 9%, 5% and 16% for different sleep stages in comparison to a baseline. All false detection at every presented sleep stage is investigated.