Kavli Affiliate: Jing Wang
| First 5 Authors: Jing Wang, Jun-En Ding, Feng Liu, Elisa Kallioniemi, Shuqiang Wang
| Summary:
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.
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