兰州大学机构库
Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm
Zheng, Xiangwei1; Yin, Xiaochun2; Shao, Xuexiao3; Li, Yalin4; Yu, Xiaomei1
2020-06-13
Source PublicationCOMPLEXITY   Impact Factor & Quartile
ISSN1076-2787
EISSN1099-0526
Volume2020
page numbers14
AbstractSleep-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.
PublisherWILEY-HINDAWI
DOI10.1155/2020/1496973
Indexed BySCIE
Language英语
Funding ProjectNational Natural Science Foundation of China[61373149][61672329]
WOS Research AreaMathematics ; Science & Technology - Other Topics
WOS SubjectMathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS IDWOS:000544650700006
PublisherWILEY-HINDAWI
Original Document TypeArticle
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/421878
Collection兰州大学
Corresponding AuthorShao, Xuexiao; Yu, Xiaomei
Affiliation
1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China
2.WeiFang Univ Sci & Technol, Facil Hort Lab Univ Shandong, Shouguang, Peoples R China
3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China
4.Shandong Management Univ, Key Lab TCM Data Cloud Serv Univ Shandong, Sch Informat Engn, Jinan 250357, Peoples R China
Recommended Citation
GB/T 7714
Zheng, Xiangwei,Yin, Xiaochun,Shao, Xuexiao,et al. Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm[J]. COMPLEXITY,2020,2020.
APA Zheng, Xiangwei,Yin, Xiaochun,Shao, Xuexiao,Li, Yalin,&Yu, Xiaomei.(2020).Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm.COMPLEXITY,2020.
MLA Zheng, Xiangwei,et al."Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm".COMPLEXITY 2020(2020).
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