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http://dx.doi.org/10.7471/ikeee.2019.23.4.1273

Prediction of Sleep Stages and Estimation of Sleep Cycle Using Accelerometer Sensor Data  

Gang, Gyeong Woo (Fusion Data, Inc.)
Kim, Tae Seon (School of Information, Communications & Electronics Engineering, The Catholic Univ. Korea)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1273-1279 More about this Journal
Abstract
Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.
Keywords
Sleep stage prediction; Sleep monitoring; Accelerometer; Actigraphy; Random forest; EEG;
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