Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reasoning ability, and behavior, especially in older people. The cause of Alzheimer's Disease is still under exploration and there is no all-inclusive theory that can explain the pathologies in each individual patient. Nevertheless, early intervention has been found to be effective in managing symptoms and slowing down the disease's progression. Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals. Prior studies have used various machine learning methods, including deep learning and graph neural networks, to examine electroencephalography-based signals for identifying Alzheimer's Disease patients. In our research, we proposed a Flexible and Explainable Gated Graph Convolutional Network (GGCN) with Multi-Objective Tree-Structured Parzen Estimator (MOTPE) hyperparameter tuning. This provides a flexible solution that efficiently identifies the optimal number of GGCN blocks to achieve the optimized precision, specificity, and recall outcomes, as well as the optimized area under the Receiver Operating Characteristic (AUC). Our findings demonstrated a high efficacy with an over 0.9 Receiver Operating Characteristic score, alongside precision, specificity, and recall scores in distinguishing health control with Alzheimer's Disease patients in Moderate to Severe Dementia using the power spectrum density (PSD) of electroencephalography signals across various frequency bands. Moreover, our research enhanced the interpretability of the embedded adjacency matrices, revealing connectivity differences in frontal and parietal brain regions between Alzheimer's patients and healthy individuals.