Deep Learning for EEG‐Based Alzheimer’s Disease Diagnosis

Abstract Background Electroencephalography (EEG) could be a powerful tool to diagnose Alzheimer’s Disease (AD) because it is pervasive, non‐invasive, and cost‐effective [1]. However, EEG‐based AD detection has suffered from poor performance and low generalizability due to the noisy nature of the signal and high inter‐patient variability. Here, we show that emerging deep learning techniques can alleviate these challenges and pave the way for the clinical use of EEG in AD detection. Method We formulate the problem of EEG‐based AD detection as a binary classification task. We propose an end‐to‐end deep neural network that utilizes contrastive representation learning to automatically learn low‐dimensional features from EEG trials. These features are then fed into a non‐linear classifier that predicts the probability of AD. Result We evaluated the model on a public dataset comprising 23 subjects (12 AD; 11 control) that have 663 trials in total [2]. (1) Patient‐dependent setup. In line with existing work in EEG‐based AD detection, we randomly assigned 80% of trials for training and the remaining 20% for tests, where the same patient could have trials in both groups. Our model achieved an F1 score of 99.35% which is competitive with, if not superior to, the state‐of‐the‐art baselines. (2) Patient‐independent setup. We then investigate a more challenging setup, where trials from a particular patient were assigned either to training (19 subjects) or to testing (4 subjects). The deep learning system is evaluated on patients it has not encountered before. Our model attains an F1 score of 86.45%, outperforming baselines by a significant margin (>20%). Conclusion Our results demonstrate that our deep learning model significantly improves the accuracy of EEG‐based AD diagnosis. In particular, our model sets the state‐of‐the‐art performance in the patient‐independent evaluation. This suggests that our model can learn a distinctive pattern of AD from a small group of subjects and apply it to unseen individuals: which is indispensable for real‐world deployment. [1] Tait, X. et al. Eeg Microstate Complexity for Aiding Early Diagnosis of Alzheimer’s Disease. Scientific Reports, 2020. [2] K. Smith, et al. Accounting for the Complex Hierarchical Topology of Eeg Phase‐Based Functional Connectivity in Network Binarisation. PloS One, 2017.