Federated GNNs for EEG-Based Stroke Assessment

TL;DR

This paper introduces a federated learning approach using Graph Neural Networks to predict stroke severity from EEG data across multiple hospitals, ensuring privacy and interpretability with high accuracy close to human experts.

Contribution

It presents a novel federated GNN framework for EEG-based stroke assessment that preserves patient privacy and offers explainability, advancing collaborative medical machine learning.

Findings

Achieved a mean absolute error of 3.23 in NIHSS prediction

Demonstrated model's performance close to human experts

Enabled multi-institutional collaboration without data sharing

Abstract

Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized treatment plans. However, outsourcing medical records to train ML models using patient data raises legal, privacy, and security concerns. Federated learning has emerged as a promising paradigm for collaborative ML, meeting healthcare institutions' requirements for robust models without sharing sensitive data and compromising patient privacy. This study proposes a novel method that combines federated learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity using electroencephalography (EEG) signals across multiple medical institutions. Our approach enables multiple hospitals to jointly train a shared GNN model on their local EEG data without exchanging patient information. Specifically, we address a regression problem by predicting the National Institutes of Health Stroke Scale (NIHSS), a key indicator of stroke severity. The proposed model leverages a masked self-attention mechanism to capture salient brain connectivity patterns and employs EdgeSHAP to provide post-hoc explanations of the neurological states after a stroke. We evaluated our method on EEG recordings from four institutions, achieving a mean absolute error (MAE) of 3.23 in predicting NIHSS, close to the average error made by human experts (MAE $\approx$ 3.0). This demonstrates the method's effectiveness in providing accurate and explainable predictions while maintaining data privacy.