Design of an Improved Model for Seizure Prediction Using Hybrid Spatio-Temporal Transformers and Graph Neural Networks

Due to the critical need to improve quality of life and control risks associated with unpredictable seizures, there is a need for accurate and early seizure prediction in epilepsy patients. The common seizure prediction methods rely on either traditional machine learning-based or recurrent neural network (RNN)-based models; both models have their limitations on the capture of long-range temporal dependencies and spatial correlations in the case of multi-channel EEG data samples. Generalization is yet another weak point of these models, especially across different patient data sets, and usually they are not flexible and adaptable enough to change character with the complexity or even the dynamic nature of seizure activity patterns. The objective of this work is to establish a novel multi-model approach based on advanced deep learning techniques to enhance the accuracy of seizure prediction. The first one was the Hybrid Spatio-Temporal Transformer Network, HSTTN, which combined the spatial feature extraction of CNNs with the powers of transformers in processing time, therefore, would yield a better long-range analysis in EEG signals. The other was the Graph Neural Network with Temporal Attention, GNN-TA, which used the graph structure in modeling EEG and introduced temporal attention for focusing on the right time window for better spatial-temporal learning. Third, Wavelet-CNN with Transfer Learning (WCT-Net) integrates wavelet transforms with CNNs in order to enhance the analysis in the time and frequency domains, while transfer learning takes advantage of adaptation over different patients. Furthermore, Bi-Directional Long Short-Term Memory with Attention Mechanism (BiLSTM-AM) captures past and future relationships in EEG signals as well as attention mechanisms focus key patterns related to the seizure. Lastly, the Auto encoder with Recurrent Neural Networks and Reinforcement Learning (AERNN-RL) learning adaptive system compresses the EEG signal and optimizes performance through reinforcement learning. With projected sensitivity, specificity, and adaptability improvements, its precision in seizure prediction highly compares with the multi-model approach, hence giving significant impacts in clinical applications for patient care and seizure management.