Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

• URL: http://arxiv.org/abs/2410.07199v1
• Date: Tue, 24 Sep 2024 14:40:09 GMT
• Title: Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
• Authors: Andrea Protani, Lorenzo Giusti, Chiara Iacovelli, Albert Sund Aillet, Diogo Reis Santos, Giuseppe Reale, Aurelia Zauli, Marco Moci, Marta Garbuglia, Pierpaolo Brutti, Pietro Caliandro, Luigi Serio,
• Abstract summary: 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. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties.
• Score: 1.2618555186247336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\delta$ (2-4 Hz), $\theta$ (4-8 Hz), $\alpha_1$ (8-10.5 Hz), $\alpha_2$ (10.5-13 Hz), and $\beta_1$ (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.

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