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

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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

Document Type: Research Article

Publication date: 01 April 2015

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  • NBE is the official journal of the Institute of Complex Medical Engineering (ICME). The journal promotes the best interdisciplinary research in neuroscience and biomedical engineering. NBE welcomes contributions in any domain of neural and biomedical engineering ranging in content from practical/clinical applications through experimental science, technological developments to quantitative and/or statistical methodology discussions. NBE publishes the following article types: original articles, research reviews, and book reviews.

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