Microsleep Classifier Using EOG Channel Recording: A Feasibility Study
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.
KeywordsMicrosleep Electrooculogram Automatic detection Classifier
This work has been supported by the project No.SGS13/203/OHK3/3T/13 of the Czech Technical University in Prague.
- 2.Peiris, M.T., Jones, R.D., Davidson, P.R., Bones, P.J.: Detecting behavioral microsleeps from EEG power spectra. In: Engineering in Medicine and Biology Society, EMBS 2006, 28th Annual International Conference of the IEEE, pp. 5723–5726. IEEE (2006)Google Scholar
- 3.Poudel, G.R., Jones, R.D., Innes, C.R., Watts, R., Signal, T.L., Bones, P.J.: fMRI correlates of behavioural microsleeps during a continuous visuomotor task. In: Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE, pp. 2919–2922. IEEE, September 2009Google Scholar
- 5.Golz, M., Sommer, D., Krajewski, J., Trutschel, U., Edwards, D.: Microsleep episodes and related crashes during overnight driving simulations. In: Proceedings of the Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (2011)Google Scholar
- 6.Czisch, M., Wehrle, R., Harsay, H.A., Wetter, T.C., Holsboer, F., Sämann, P.G., Drummond, S.P.: On the need of objective vigilance monitoring: effects of sleep loss on target detection and task-negative activity using combined EEG/fMRI. Frontiers in neurology, vol. 3 (2012)Google Scholar
- 7.Leong, W.Y., Mandic, D.P., Golz, M., Sommer, D.: Blind extraction of microsleep events. In 15th International Conference on Digital Signal Processing, pp. 207–210. IEEE, July 2007Google Scholar
- 8.Rimini-Doering, M., Altmueller, T., Ladstaetter, U., Rossmeier, M.: Effects of lane departure warning on drowsy drivers’ performance and state in a simulator. In: Proceedings of the third international driving symposium on human factors in driver assessment, training, and vehicle design, pp. 88–95, June 2005Google Scholar
- 9.Furman, G.D., Baharav, A., Cahan, C., Akselrod, S.: Early detection of falling asleep at the wheel: A heart rate variability approach. In: Computers in Cardiology, pp. 1109–1112. IEEE, September 2008Google Scholar