School of Information Science and Engineering, Yanshan University, Qinhuangdao, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China.
School of Information Science and Engineering, Yanshan University, Qinhuangdao, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China.
Neural Netw. 2020 Apr;124:373-382. doi: 10.1016/j.neunet.2020.01.025. Epub 2020 Jan 30.
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.
最近，将脑电（EEG）信号的特征提取和分类方法相结合已广泛应用于识别轻度认知障碍。然而，当结合一个分类器时，哪种 EEG 信号特征对于评估伴有 2 型糖尿病的遗忘型轻度认知障碍（aMCI）最有效仍不清楚。本研究提出了一种新的 EEG 信号特征提取方法，名为特征融合多谱图像方法（FMIM），用于诊断伴有 2 型糖尿病的 aMCI。FMIM 与卷积神经网络（CNN）相结合，对处理后的多谱图像数据进行分类。结果表明，与现有的多谱图像方法（MIM）相比，FMIM 可以有效地从对照组中识别出伴有 2 型糖尿病的 aMCI，其改进包括特征提取的类型和数量。同时，在分类过程中可以避免部分无效计算。此外，基于 FMIM-1 的数据集在 Alpha2-Beta1-Beta2 频段组合下，基于 FMIM-2 的数据集在 Theta-Alpha1-Alpha2-Beta1-Beta2 频段组合下的分类评估指标最佳。因此，FMIM 可以作为一种有效的伴有 2 型糖尿病的 aMCI 特征提取方法，也是临床应用中有价值的生物标志物。
