Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
This work addresses stroke severity estimation for healthcare professionals, offering an explainable method that is incremental over existing graph theory and GNN techniques.
The study tackled predicting stroke severity using EEG signals by proposing a Graph Neural Network approach, achieving insights into brain reconfiguration and providing a tool for clinical diagnosis and treatment.
After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $δ$ (2-4 Hz), $θ$ (4-8 Hz), $α_1$ (8-10.5 Hz), $α_2$ (10.5-13 Hz), and $β_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation.