A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals

Background: The diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) has garnered more attention recently. New methods: In this paper, we present a novel approach for the diagnosis of AD, in terms of classifying the resting-state EEG of AD, mild cognitive impairment (MCI), and healthy control (HC). To overcome the hurdles of limited data available and the over-fitting problem of the deep learning models, we studied overlapping sliding windows to augment the one-dimensional EEG data of 100 subjects (including 49 AD subjects, 37 MCI subjects and 14 HC subjects). After constructing the appropriate dataset, the modified DPCNN was used to classify the augmented EEG. Furthermore, the model performance was evaluated by 5 times of 5-fold cross-validation and the confusion matrix has been obtained. Results: The average accuracy rate of the model for classifying AD, MCI, and HC is 97.10%, and the F1 score of the three-class classification model is 97.11%, which further proves the model's excellent performance. Conclusions: Therefore, the DPCNN proposed in this paper can accurately classify the one-dimensional EEG of AD and is worthy of reference for the diagnosis of the disease.