Interpretable Multimodal EEG-ECG Framework for Neonatal Seizure Prediction and Patient-Adaptive Transfer Learning
This study developed a patient-independent Convolutional Neural Network (CNN) model to predict neonatal seizures by analyzing EEG and ECG signals. The model uses a reduced-montage EEG input and incorporates an attention mechanism for better feature focus. Results show high accuracy, sensitivity, and specificity, demonstrating strong generalization capabilities through transfer learning when applied to new subjects.