Microsleep classifier using EOG channel recording: A feasibility study

Abstract

The microsleeps (MS) cause many accidents and can have a huge social impact. Automated prediction or early detection of the MS states could help to monitor level of fatigue. An automated MS classifier based on the EOG signal is proposed. There were analysed 28 episodes of MS. We observed slow eye movements without rapid changes during MS episodes. An automated feature extraction and classification using EOG channels showed promising results (sensitivity 93%, positive predictivity 57%). To confirm the hypothesis it is crucial to extend the study and to analyse larger amount of MS data.