The daytime nap sleep has positive relaxation function when the subject is waking up
from about 20 minutes light sleep, but negative effect of sleep inertia when they fall and wake up
from deep sleep. In this study, an automatic sleep level prediction method was developed for daytime
short nap regulation. The ultimate purpose is to predict the tendency of sleep level from light to deep. Accordingly
the subject can have mode refreshed by waking up from light sleep. The sleep data during nap in the afternoon was
recorded. Totally, 8 subjects participated. The sleep level is described by two parameters of EEG (Electroencephalography)
calculated for each 5-second segment data. ARMA (Auto-Regressive and Moving Average) model is
adopted for sleep level prediction. In order to evaluate the effectiveness of prediction results, SVM (Supported Vector
Machine) is utilized to make sleep stage classification. The obtained classification results were compared with the
visual inspection. The accuracy was with an averaged value of 80%. The developed method was useful for the estimation
and prediction of sleep level variation during one’s nap.
Keywords: ARMA model, EEG, Nap, sleep level.
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