机构：[1]Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China[2]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China神经科系统神经内科首都医科大学宣武医院[3]Handan First Central Hospital, Handan, China
Objective: To achieve the early diagnosis of amnestic mild cognitive impairment (aMCI), this paper proposes a multi-dimensional index, which combines the advantages of the multiscale fuzzy entropy (FuzzyEn) and phase locking value (PLV) based on electroencephalography (EEG). Methods: The complexity and synchronization of the EEG were characterized using FuzzyEn and PLV in five frequency bands, respectively. By combining the two methods, the changes in the health of brain function were comprehensively analyzed. The extreme learning machine (ELM) method was used to classify aMCI patients based on a multi-dimensional index. Results: Compared with aMCI patients, the multiscale FuzzyEn and PLV of normal controls (NC) were higher and statistically significant (P < 0.05) in the Fp1 and Fp2 channels. Moreover, significant correlation existed between the multiscale FuzzyEn or PLV and the MoCA scores in the Fp1 and Fp2 channels. The classification accuracy and running time based on ELM in the prefrontal lobe were 83.34% and 0.003 s, respectively. Concludes: The multi-dimensional index based on prefrontal lobe could diagnosis cognitive decline of aMCI patients. Significance: The results showed that features integrated multiscale FuzzyEn and PLV could be used as a biomarker of cognitive decline and help realize the early diagnosis of aMCI patients.
基金：
China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2014M550582]; Key Project of Natural Science Foundation of Hebei Province [F2019203515, F2018203256]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61633018, 82020108013, 62076216, 61827811, U20A20192]
第一作者机构：[1]Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
通讯作者：
通讯机构：[1]Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China[*1]Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
推荐引用方式(GB/T 7714)：
Su Rui,Li Xin,Li Zhenyang,et al.Constructing biomarker for early diagnosis of aMCI based on combination of multiscale fuzzy entropy and functional brain connectivity[J].BIOMEDICAL SIGNAL PROCESSING AND CONTROL.2021,70:doi:10.1016/j.bspc.2021.103000.
APA：
Su, Rui,Li, Xin,Li, Zhenyang,Han, Ying,Cui, Wei...&Liu, Yi.(2021).Constructing biomarker for early diagnosis of aMCI based on combination of multiscale fuzzy entropy and functional brain connectivity.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,70,
MLA：
Su, Rui,et al."Constructing biomarker for early diagnosis of aMCI based on combination of multiscale fuzzy entropy and functional brain connectivity".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 70.(2021)
