Constructing biomarker for early diagnosis of aMCI based on combination of multiscale fuzzy entropy and functional brain connectivity

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.