An Enhanced Broad Learning System with Mean Time Series Difference for Aided Diagnosis of Mild Cognitive Impairment

Jing Wang, Yong Jun He, C. L. Philip Chen, Xiping Jia, Zhiyong Lin, Huiming Zhao

Abstract: Alzheimer's disease is a progressive neurological disorder. The disease is not reversible, but mild cognitive impairment is a transitional state between Alzheimer's disease and cognitively normal, it is a stage in which the brain is so minimally diseased that it can be treated or slowed or prevented from developing further lesions. At present, there are many methods for the aided diagnosis of mild cognitive impairment, among which the method based on fMRI medical images has emerged in recent years. However, the current aided diagnosis methods in this direction have some problems such as low accuracy and cumbersome feature extraction. A novel feature extraction method is put forward, namely the mean time series difference method in this paper. The feature extraction method effectively improves the accuracy in the auxiliary diagnosis task of mild cognitive impairment. An enhanced broad learning system with mean time series difference also was proposed. The results show that the enhanced broad learning system with mean time series can not only effectively optimize the feature extraction process, but also the accuracy of mild cognitive impairment classification tasks., which has significance for the clinical auxiliary diagnosis of mild cognitive impairment.