Interpretable Multimodal EEG-ECG Framework for Neonatal Seizure Prediction and Patient-Adaptive Transfer Learning

Neonatal seizures carry substantial risks of acute brain injury and long-term neurodevelopmental impairment, motivating the need for timely identification of impending seizure activity. While continuous electroencephalogram (EEG) monitoring remains the clinical gold standard, reliable patient-independent seizure prediction in neonates remains largely under-explored. We propose an interpretable multimodal framework that combines EEG and electrocardiogram (ECG) signals for neonatal seizure prediction. Our method uses mel-frequency cepstral coefficient (MFCC) representations processed by a lightweight (approx. 70k-parameter) convolutional neural network with squeeze-and-excitation attention. The framework was evaluated on the Helsinki neonatal EEG-ECG dataset and the Siena scalp EEG-ECG dataset. Under strict leave-one-patient-out (LOPO) evaluation, patient-independent prediction proved challenging (mean accuracy 58.5%, F1-score 60.3%), highlighting substantial inter-subject variability in neonatal EEG. However, fine-tuning the pretrained model with only five minutes of patient-specific data per class improved performance to 95.87% accuracy and 95.85% F1-score, demonstrating the feasibility of rapid patient-adaptive transfer learning. SHapley Additive exPlanations (SHAP) identified informative EEG channels that showed partial spatial agreement with clinically reported seizure origins. These findings suggest that fully patient-independent neonatal seizure prediction remains challenging, but that patient-adaptive fine-tuning with a small amount of data may provide a practical pathway toward personalized neonatal seizure prediction systems.