EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction
Researchers have developed EEG-FuseFormer, a novel framework utilizing transformer architecture for predicting seizure onset in epilepsy patients. This model integrates features from CNN-LSTM and ResNet-18 networks, achieving a mean recall of 98.85% on the CHB-MIT dataset. The study also explored target adaptation techniques to improve cross-patient testing performance and analyzed the model's computational complexity. AI
IMPACT This model demonstrates a significant advancement in AI-driven medical diagnostics, potentially improving patient safety and quality of life.