Objective. The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively. Approach. In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method. Main results. The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better t...

中文翻译：

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基于多光谱图像和卷积神经网络的2型糖尿病轻度轻度认知障碍静息状态脑电信号分类。

目的。这项研究的目的是判断这种多光谱图像和卷积神经网络（CNN）方法的组合方法是否可用于区分2型糖尿病（T2DM）和正常对照（NC）的轻度轻度认知障碍（aMCI） T2DM有效。方法。在这项研究中，作者首先在5个不同的频段（包括Theta，Alpha1，Alpha2，Beta1和Beta2）上合并了来自aMCI的T2DM和NC患者与T2DM的脑电信号。然后，作者将这些时间序列转换为一系列多光谱图像。最后，利用CNN方法对图像数据进行分类。主要结果。在以下三个频段组合上可获得高达89％，91％和92％的分类效果：Theta，Alpha1和Alpha2；Alpha1，Alpha2和Beta1；和Alpha2，Beta1和Beta2。