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Neuroscience and Biomedical Engineering (Discontinued)


ISSN (Print): 2213-3852
ISSN (Online): 2213-3860

Sleep Level Prediction for Daytime Short Nap Based on Auto-Regressive Moving Average Model

Author(s): Bei Wang, Junmin Zhang, Tao Zhang, Takenao Sugi, Xingyu Wang and Masatoshi Nakamura

Volume 3 , Issue 1 , 2015

Page: [34 - 39] Pages: 6

DOI: 10.2174/2213385203666150710184747

Price: $65


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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