Abstract
The classification of sleep stages based on EEG signals has become a prerequisite for monitoring sleep quality and diagnosing sleep-related diseases. Many researchers have conducted related research work. But, they often overlook the effect of the extracted characteristics on actual sleep staging results and the interpretation in psychology and clinical medicine. Therefore, this study calculates the value of multiscale fuzzy entropy as evaluation criteria and measures the threshold range of sleep stage based on CEEMDAN algorithm and psychophysics method. The experimental results show that the proposed method can effectively distinguish between different sleep stages by using fuzzy entropy as a measure of sleep staging thresholds. In addition, we designed a set of comparative experiments based on the single-channel EEG sample data and studied the gender factor on sleep stages by comparing sleep entropy thresholds of different genders. It was found that the sleep threshold of female was significantly greater than male.
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References
Chen, X.: Automatic sleep staging based on EEG. Nanjing University of Posts and Telecommunications (2014)
Loomis, W.E., Shull, C.A., Snedecor, G.W.: Methods in Plant Physiology: A Laboratory Manual and Research Handbook. McGraw-Hill, New York City (1937)
Shao, X., Hu, B., Zheng, X.: A study on automatic sleep stage classification based on clustering algorithm. In: Zeng, Y., et al. (eds.) BI 2017. LNCS (LNAI), vol. 10654, pp. 139–148. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70772-3_13
Tang, Q.: Automatic sleep staging based on EEG signals. Guangdong University of Technology (2016)
Cheng, J.: Sleep stage analysis based on EEG signals. Beijing Institute of Technology (2015)
Hassan, A.R., Bhuiyan, M.I.H.: Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed. Signal Process. Control 24, 1–10 (2016)
Tiantian, L., Yong, L.: Measurement of thresholds in facial expressions and their age and gender differences. Psychol. Behav. Res. 13(6), 771–777 (2015)
Jinde, Z., Minjun, C., Junsheng, C., et al.: Multi-scale fuzzy entropy and its application in fault diagnosis of rolling bearings. J. Vib. Eng. 27(1), 145–151 (2014)
Lee, T.M., Liu, H.L., Hoosain, R., et al.: Gender differences in neural correlates of recognition of happy and sad faces in humans assessed by functional magnetic resonance imaging. Neurosci. Lett. 333(1), 13–16 (2002)
Gur, R.C., Gunningdixon, F., Bilker, W.B., et al.: Sex differences in temporo-limbic and frontal brain volumes of healthy adults. Cereb. Cortex 12(9), 998–1003 (2002)
Acknowledgments
National Natural Science Foundation of China (61373149) and the Taishan Scholars Program of Shandong Province, China.
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Shao, X., Hu, B., Li, Y., Zheng, X. (2018). A Study of Sleep Stages Threshold Based on Multiscale Fuzzy Entropy. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_19
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DOI: https://doi.org/10.1007/978-3-030-05057-3_19
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