A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia

In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18%, 69.03 ± 1.33%, 85.34 ± 1.86% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.