Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder Using Deep Learning

Abstract

Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentions to lower the cost of scoring. Such computeraided diagnosis of sleep disorders are usually based on the 6 scores, wake (W), sleep status (S1-S4) and REM by labelling every 30-second long EOG records. This paper presents an automatic scoring method of sleep stages by using the recent advancements in deep learning. We also suggest an interactive scoring scheme to enable the doctors of practitioners to give feedback by correcting errors and improve the accuracy of scoring as well as diagnosis of sleep disorders.