Novel deep learning framework with hierarchical temporal feature refinement for enhanced epileptic seizure detection

This is the authors' abstract. We don't add key points for this paper.

Epileptic seizure detection remains a significant challenge in the field of neurology due to the complexity and variability of electroencephalogram (EEG) signals. Traditional manual methods of analyzing EEG data are time-consuming, subjective, and prone to misdiagnosis, particularly in long-term recordings. To address these challenges, this study proposes a novel hierarchical temporal feature refinement network for epileptic seizure detection (HTFRNet-ESD) framework designed to enhance the accuracy and efficiency of epileptic seizure detection. The methodology comprises three key components: a spatial-temporal signal encoder (STSE) for capturing spatio-temporal attributes, an adaptive temporal dynamics encoder (ATDE) for detailed temporal feature analysis, and a hierarchical temporal refinement unit (HTRU) module for integrating frequency-driven power features. The proposed model leverages advanced deep learning techniques, including convolutional neural networks (CNNs) and transformers, to effectively learn both coarse and fine-grained temporal dynamics of EEG signals. Evaluation on publicly available EEG datasets demonstrates the model’s superior sensitivity, specificity, and overall accuracy compared to existing approaches. The HTFRNet-ESD sets a new benchmark for automated seizure detection, offering a reliable and scalable solution for real-world clinical applications. This work paves the way for improved patient outcomes through timely and precise epilepsy diagnosis.