EEG Sleep Detection Algorithms a Comparative Study
Abu Saleh Musa Miah , Samiha Sahadat, Dr. Mamunur Rashid
In this paper it represents a comparison between some machine learning algorithm which is applied to solve the sleep monitoring issues. Sleep detection requires electroencephalogram signal for differentiating purpose. There are some methods & models that are already perused & built with training & testing datasets like- single layer perception, multilayer perception, SVM (support vector machine),boosted tree method. The difference between these models is measured using the Cohen’s index, the true positive & the true negative rate. Cross-validation technique is usually used to weigh the results of the models of monitoring sleep. The models successfully monitors sleep state reaching up to 94% and Cohen’s index successfully reaching up to 0.69.The success rate shows the considerable assurance for future expansion & practices.
EEG signal, Sleep stagine in EEG, feature extraction, EEG signal classification
 National Highway Traffic Safety Administration. Drowsy Driving and Automobile Crashes. http://www.nhtsa.gov/people/injury/drowsy driving1/ Drowsy.html (Accessed 08 May, 2014).
 1.9 Million Drivers Have Fatigue-Related Car Crashes or Near Misses Each Year. National Sleep Foundation (2009). http://www.sleepfoundation.org/media-center/press-re lease/ 19-million-drivers-have-fatigue-related-car-crashes-or -near-misses-each (Accessed 08 May, 2014).
 Crashes Where Fatigue Was a Contributing Factor. National Sleep Foundation (2012). http://sleepfoundation.org/sites/default/files/Crashes% 20Fatigue%20a%20Factor.pdf (Accessed 20 May, 2015).
 Zhovna I. and Shallom I. D.: Automatic detection and classification of sleep stages by multichannel EEG signal modelling. Engineering in Medicine andBiology Society, 2008. 30th Annual International Conference of the IEEE, 2665-2668 (2008).
 Devuyst S., Dutoit T., Kerkhofs M.: DREAMS Project, The DREAMS Sleep Subjects Database. http://www.tcts.fpms.ac.be/∼devuyst/Databases/ DatabaseSubjects/ (Accessed 16 April, 2014).
 KDnuggets Polls, Data Mining Methodology (2007). http://www. kdnuggets.com/polls/2007/data mining methodology.htm (Accessed 20 May, 2015).
 Van Hese P., Philips W., De Koninck J., Van de Walle R., Lemahieu I.: Automatic detection of sleep stages using the EEG. Engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE, 2, 1944-1947 (2001).
 Malaekah E. and Cvetkovic D.: Automatic detection of the wake and stage 1 sleep stages using the EEG sub-epoch approach. Engineering in Medicine and Biology Society (EMBC) 2013, 35th Annual International Conference of the IEEE, 6401-6404 (2013).
 Rechtschaffen A., Kales A.: A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. US Department of Health, Education, and Welfare Public Health Service (1968).
Iber C., Ancoli-Israel S., Chesson Jr. A. L., Quan S. F.: The AASM Manual for the Scoring of Sleep and Associated Events. American Academy of Sleep Medicine (2007). Sucholeiki R.: Normal EEG Waveforms (2014). http://emedicine. medscape.com/article/1139332-overview#aw2aab6b3 (Accessed 14 April, 2014).
Lalkhen A. G. and McCluskey A.: Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia, Critical Care & Pain, 8, 221223 (2008).
Cohen J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37-46 (1960).
 Kennedy J. and Eberhart R.: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942 (1995).
Pasieczna A. H.: An Approach to Driver Sleep Detection. Master Thesis Report, Wrocław University of Economics (2015).
Boser B. E., Guyon I. M., Vapnik V.: A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, 144-152 (1992).