Personalized Real-Time Sleep Stage from Past Sleep Data to Today’s Sleep Estimation
This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males (whose age are 20 and 40) and one adult female (whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it.
KeywordsSleep stage estimation Real-time monitoring Evolutionary computation
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