The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method

Abstract

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.

Keywords: Convolutional neural network; EEG signal; Feature-fusion multispectral image; aMCI with T2DM.