Research articles
ScienceAsia 43 (2017): 312-318 |doi:
10.2306/scienceasia1513-1874.2017.43.312
Improved GP algorithm for the analysis of sleep stages based on grey model
Yabing Li, Songyun Xie*, Jin Zhao, Chang Liu, Xinzhou Xie
ABSTRACT: Correlation dimension analysis of EEG signals is widely used to access sleep stages. However, the standard Grassberger-Procaccia (GP) algorithm used to calculate the correlation dimension is very time consuming. To overcome this problem, an algorithm that combines the grey model and GP algorithm (GM-GP) is proposed. The results show that the correlation dimensions computed from GP and GM-GP are highly correlated, and the significance between the CDs in different stages of GM-GP is similar to GP. Furthermore, the computation time of the proposed method is at most 5% of that of the GP. The proposed algorithm is suitable for the real-time monitoring of sleep stages, which can provide a deeper understanding of brain function.
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| NPU-TUP Joint Laboratory for Neural Informatics, Northwestern Polytechnical University, Xi'an, China, 710129 |
* Corresponding author, E-mail: syxie@nwpu.edu.cn
Received 6 Jun 2017, Accepted 22 Nov 2017
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