Cross-Site Generalization Using Attention Layer for Epileptic Seizure Detection

Despite the growing body of research on automated epileptic seizure detection, clinical adoption of these techniques remains limited. This can be attributed to several factors, especially the time-intensive nature of model training, and the lack of generalizability to diverse patient populations. To overcome these challenges, our paper introduces a hybrid CNN-LSTM-AT model designed to exhibit robustness in cross-site variability. The model employs a one-dimensional convolutional neural network (1D CNN) to leverage the temporal dynamics within EEG data, extracting informative features that capture the sequential variations in brain activity. These extracted features are subsequently fed into a long short-term memory (LSTM) module, complemented by an attention (AT) mechanism, to harness the LSTM's memory capabilities and enhance feature relevance. An AT layer is strategically incorporated post-LSTM module to prioritize critical input parameters, thus reducing complexity and time. To mitigate the issue of cross-site variability, the model is trained on the publicly available Children's Hospital of Boston (CHB- MIT) dataset and rigorously evaluated on a French dataset acquired from the Centre Hospitalier Universi-taire of Angers (CHU). Experimental results demonstrate that our proposed approach surpasses state-of-the-art methods. As far as our knowledge extends, this study represents the first attempt to address cross-site datasets while incorporating the AT mechanism.