%0 Journal Article
%T STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis.
%A Zhou H
%A Yin L
%A Su R
%A Zhang Y
%A Yuan Y
%A Xie P
%A Li X
%J Comput Methods Programs Biomed
%V 248
%N 0
%D 2024 May 8
%M 38471292
%F 7.027
%R 10.1016/j.cmpb.2024.108123
%X <B>OBJECTIVE: </B>Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU).<BR><B>METHODS: </B>The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module.<BR><B>RESULTS: </B>Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989.<BR><B>CONCLUSIONS: </B>The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.