Physics-informed graph neural networks for robust cross-patient epileptic seizure prediction via chimera state...

Epilepsy affects approximately 50 million individuals worldwide, with 30% experiencing drug-resistant seizures despite optimal pharmacological management. Recent computational neuroscience advances have identified chimera states-spatiotemporal patterns where synchronized and desynchronized neural dynamics coexist-as potential biomarkers preceding seizures by 15-90 minutes. However, clinical translation faces critical challenges: (1) existing detection methods require extensive manual parameter optimization limiting scalability, (2) machine learning approaches show 20-35% accuracy degradation when applied to new patients, and (3) deep learning models lack the interpretability required for clinical validation. This paper seeks to answer the question: Can integrating physics-based constraints from Kuramoto oscillator theory with graph neural networks enable automated, robust, and interpretable chimera-based seizure prediction that generalizes across patients?.

We developed HP-GNN (Hybrid Physics-Informed Graph Neural Network), a novel architecture integrating data-driven learning with Kuramoto oscillator dynamics. The framework transforms multi-channel EEG into dynamic hypergraphs capturing higher-order neural interactions through: (1) adaptive hypergraph construction using Phase Locking Values with threshold τ = 0.65 for 3-clique detection, (2) three-layer hypergraph convolutions (64 → 128 → 256 dimensions), (3) Mamba state space networks achieving linear O(T) complexity, (4) physics-informed regularization with Kuramoto dynamics (weight λ₁ = 0.03), and (5) multi-task prediction heads. We employed two-stage training: self-supervised pre-training on 844 hours of continuous EEG, followed by supervised fine- tuning. Evaluation used 4-fold cross-validation on CHB-MIT (22 pediatric patients, 182 seizures) with external validation on IEEG.org (16 adults, 87 seizures).

HP-GNN achieved 84.7% chimera detection accuracy (95% CI: 82.3-87.1%), representing 9.2% improvement over Delay Differential Analysis (75.5%, p < 0.001). Seizure prediction demonstrated 89.3% sensitivity with 68.2% maintained at 90-minute horizons, achieving 0.48 false positives per hour. Cross-patient generalization reached 79.8%, improving 14.6% over graph baselines. Physics constraints reduced training requirements by 35% (achieving 80% accuracy with 260 vs 400 patient- hours). Zero-shot transfer from scalp to intracranial recordings achieved 71.3% accuracy. GNNExplainer identified critical electrodes with κ = 0.68 agreement with neurologists. Learned parameters showed biological plausibility: synchronized components at 2.3 ± 0.5 Hz (delta), desynchronized at 9.1 ± 1.3 Hz (alpha).

Integrating physics-based constraints with graph neural networks enables robust seizure prediction addressing key deployment barriers. The combination of improved performance, cross- patient generalization, data efficiency, and clinical interpretability positions HP-GNN as a promising foundation for clinical seizure forecasting systems.