轻度认知障碍患者EEG动力学特征提取与分类方法研究进展

doi:10.3969/j.issn.0258-8021.2019.03.012

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Abstract Electroencephalography (EEG) can be used to assess pathological changes in mild cognitive impairment (MCI) disorders. In recent years, the feature extraction and classification methods in the field of EEG have been widely applied to the diagnosis of mild cognitive impairment diseases. In order to understand the current development of MCI EEG signal analysis, this work first analyzed in depth the application of EEG signal from MCI patients in the field of feature extraction, its advantages and disadvantages from two aspects of local coupling and global synchronization. Then the method of classification of EEG signal from MCI patients was summarized and analyzed, such as support vector machines, K-means, and convolutional neural networks that have been widely used in recent years. Finally, the future development trend of dynamic feature extraction and classification methods was prospected

Key words： mild cognitive impairment EEG signals dynamics feature extraction classification

Received: 13 August 2018

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R318

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	Articles by authors
	Li Hengzhi
	Wen Dong
	Wei Zhenhao
	Zhou Yanhong

Cite this article:

Li Hengzhi,Wen Dong,Wei Zhenhao, et al. Advances in the Extraction and Classification of EEG Dynamic Features in Patients with Mild Cognitive Impairment[J]. Chinese Journal of Biomedical Engineering, 2019, 38(3): 348-354.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.03.012 OR http://cjbme.csbme.org/EN/Y2019/V38/I3/348

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