Abstract

Sleep deprivation of high school and university students is currently an actual concerning issue. Sleep deprivation is one of the leading causes of dozing-off during daytime. Usually, the state of drowsiness is very little concerned, but studies on drowsiness show the importance of investigating the frequency of occurrence as well as the need to clarify the cause and propose limited measures appeared dozing-off. Dozing-off is not only an undesirable state and disrupts daily activities, but also provides information on personal health status. In that case, early alertness for dozing-off event is very helpful in preventing unwanted consequences. The study has designed a process to record dozing-off event, then constructed and implemented the hypnogram processing program that evaluated quantitative changes in polysomnography signals at sleep onset, the transition time from wake stage to sleep stage. By analyzing the energy spectrum of the signal and using wavelet transform in combine with the support vector machine algorithm, the research allows a comprehensive evaluation of the state of dozing-off. Determining the exact time of onset of sleep is very important in the study of drowsiness. Extracting the time of this event appears to help develop an application for early warning dozing-off. Besides, it allows making an initial assessment of the condition of the subject when the time of drowsiness begins suddenly. Six recordings from volunteered students were processed from a 45-minute vigilance test. All of the volunteers had no neuropathy and were well explained to the procedures used in this study. The results show that depending on kinds of different applications, signals such as EEG, EOG, or EMG are used individually or in combination to fetch suitable results. The result presented a successful method to distinguish dozing-off event with other stages.