Graph Attention Networks for Detecting Epilepsy from EEG Signals Using Accessible Hardware in Low-Resource Settings

TL;DR

This paper introduces a graph attention network-based method for detecting epilepsy from low-cost EEG signals in low-resource settings, emphasizing accessibility, explainability, and deployment on affordable hardware.

Contribution

It presents a novel graph-based deep learning framework tailored for low-fidelity EEG data, adaptable to affordable devices, and capable of identifying key epilepsy biomarkers.

Findings

Outperforms standard classifiers in accuracy and robustness

Highlights specific fronto-temporal connectivity biomarkers

Demonstrates effective deployment on Raspberry Pi devices

Abstract

Goal: Epilepsy remains under-diagnosed in low-income countries due to scarce neurologists and costly diagnostic tools. We propose a graph-based deep learning framework to detect epilepsy from low-cost Electroencephalography (EEG) hardware, tested on recordings from Nigeria and Guinea-Bissau. Our focus is on fair, accessible automatic assessment and explainability to shed light on epilepsy biomarkers. Methods: We model EEG signals as spatio-temporal graphs, classify them, and identify interchannel relationships and temporal dynamics using graph attention networks (GAT). To emphasize connectivity biomarkers, we adapt the inherently node-focused GAT to analyze edges. We also designed signal preprocessing for low-fidelity recordings and a lightweight GAT architecture trained on Google Colab and deployed on RaspberryPi devices. Results: The approach achieves promising classification performance, outperforming a standard classifier based on random forest and graph convolutional networks in terms of accuracy and robustness over multiple sessions, but also highlighting specific connections in the fronto-temporal region. Conclusions: The results highlight the potential of GATs to provide insightful and scalable diagnostic support for epilepsy in underserved regions, paving the way for affordable and accessible neurodiagnostic tools.