Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings

The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective appliance of prognosis of Alzheimer’s disease (AD). Electroencephalogram (EEG) has many advantages compared to other methods in the analysis of AD in an early stage. In this paper, two different deep learning (DL) architectures, including modified convolutional (CNN) and convolutional autoencoder (Conv-AE) neural networks (NNs), are proposed for classifying subjects into AD, mild cognitive impairment (MCI), and healthy control (HC) data based on scalp EEG recordings. The database includes 19-channel EEG recorded from 61 healthy control, 56 MCI, and 63 AD subjects. Time–frequency representation (TFR) is used to extract desirable features from EEG signals. Continuous wavelet transform (CWT) with Mexican hat function (MHf) as its mother wavelet is used for the selected TFR. The average accuracy obtained for the modified convolutional network and the convolutional auto-encoder network are 92% and 89%, respectively. The proposed networks in this study have superiority over those in similar studies not only by providing 10% increase in classification accuracy but also by improving the number of classes for similar data. In addition, the obtained accuracy of our networks was significantly higher than that of conventional machine learning methods. We believe the results illustrate DL architectures to be a good tool to handle EEG analysis, because of the ability to deal directly with inaccurate, inconsistent, and Para complete data, thereby providing a practical analysis.