Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm. (13th June 2020)

• Record Type: Journal Article
• Authors: Zheng, Xiangwei; Yin, Xiaochun; Shao, Xuexiao; Li, Yalin; Yu, Xiaomei
• Other Names: Zhang Xuyun Guest Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-06-13
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/1496973
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of "frequency-domain correlation coefficient" is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.