| Summary: | 碩士 === 國立中山大學 === 機械與機電工程學系研究所 === 98 === Sleep staging can be used to assess whether sleep structure is abnormal. According to the R&K rule, human sleep can be divided into four different stages: Awake, Light Sleep, Deep Sleep and Rapid-Eye-Movement (REM) Sleep. Conventionally, sleep staging are scored mainly by EEG signals and complementally by EOG and EMG signals.
The goal of this study is to detect slow wave sleep (SWS) automatically by using different combinations of EEG, EOG, and EMG signals. In particular, a total of 16 combinations of channels have been studied. Based on high amplitude slow wave characteristics of SWS, this study develops many of feature variables to characterize SWS. A subset of these features are employed to design neural network classifier to detect SWS. This study has noted interpersonal-differences in physiological signals between people and proposes solutions to this problem to improve the performance of SWS detection. The number of tested subjects from two different sleep centers is 1318 and 947 subjects, respectively. These subjects were divided into five groups for training and testing data in order to test performance of our proposed approach. By applying the proposed approach to 1318 subjects, the experimental results show that the proposed method achieves kappa of 0.63 by using a single EEG channel, kappa of 0.6 by using two channels EOG and kappa of 0.66 by using the best combination of multi-channel singals. The size of dataset used in this work is significantly large than those of previous studies and thus provide more reliable experimental results. The experimental results show that the proposed approach can provide satisfactory performance in dealing with dataset with more than 1000 subjects.
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