Comprehensive Summary
This study presents a review of the advancement and trends of deep learning (DL) techniques within the area of epilepsy research, especially, within electroencephalogram (EEG) processing and brain imaging. The authors conducted a bibliometric and visual analysis of the literature by searching the Web of Science Core Collection for published English-language original research and review articles from 2006 to 2025, and then analyzed the results using CiteSpace, VOSviewer, and Bibliometrix for trends in publication sources, collaborations, and areas of research (PMC). The authors retrieved 1,266 relevant articles that were published by institutions from 2,290 country/regions, where China and the U.S. produced the majority of the published papers, and observed a steady increase in the publication rate over time. The study highlighted key authors (i.e., Acharya, U. Rajendra) and journals (i.e., Biomedical Signal Processing and Control) in the field. Additionally, they point to the current trends in the research area of DL-based seizure detection, seizure prediction, and multi-modal integration as areas of focus. The review also suggested that multi-modal integration and interpretability of DL models as emerging trends in the research space. While the discussion highlights the greatest potential for DL to enhance the accuracy and reliability of epileptic diagnosis and prognosis, there is a clear note that much work on model interpretability, model robustness, and translatable clinical work remain.
Outcomes and Implications
These findings are significant because it offers a systematic, overarching perspective on the current state of the field, identifies gaps, and outlines areas in which future work may be focused. Clinically, the study is relevant because DL methods have the potential to be more accurate in detecting and predicting seizures, which could translate into improved monitoring, earlier intervention, and personalized treatment. However, the authors warn that there are still challenges before DL methods are deployed into widespread clinical use—especially with regard to establishing applicability across different patient populations, interpretability, and safety, and carrying out prospective clinical trials—which means that practical use may be a number of years away.