Yiman Wu, Wenqi Wu
Medical Imaging Institute, Jiangsu Medical College, Yancheng, China.
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
Digit Health. 2025 Sep 12;11:20552076251375840. doi: 10.1177/20552076251375840. eCollection 2025 Jan-Dec.
Epilepsy is a prevalent chronic neurological disease with significant impacts. Traditional diagnostic methods and machine learning face challenges due to the complexity of electroencephalography (EEG). Deep learning (DL) shows potential in epilepsy management through EEG processing and brain imaging analysis. This study aims to fill the gap of missing bibliometric analysis in this field by conducting a visual bibliometric analysis of DL applications in epilepsy, revealing research trends, hotspots, and cutting-edge developments to guide future research directions.
In this study, a comprehensive search of original research articles and reviews, limited to English-language, published from 2006 to 2025, was conducted by the Web of Science Core Collection database. Bibliometric analyses and visualizations were conducted by CiteSpace, VOSviewer, and Bibliometrix.
This search retrieved 1266 papers related to DL applied in epilepsy from the Web of Science Core Collection database, showing a consistent upward trend. These papers were from 1957 organizations across 290 countries/regions, primarily from China and the United States. "Biomedical Signal Processing and Control" ranked as the journal with the most published papers. Acharya, U. Rajendra from Ngee Ann Polytechnic, was the most authoritative author. DL-based seizure detection, prediction, and epilepsy management are key research hotspots. Moreover, multimodal data integration approaches are gaining more attention.
This study innovatively employs bibliometric methods to visually analyze research on DL applications in epilepsy, which reveals a rising trend in research within the field and identifies key hotspots, such as DL in seizure detection and prediction. The potential of DL to enhance epilepsy management is highlighted, particularly in improving the accuracy of seizure detection and prediction, thereby enhancing patients' quality of life. Furthermore, the findings highlight the importance of increasing seizure prediction accuracy, exploring multimodal data integration, and developing more interpretable DL models for future research.
癫痫是一种普遍存在的慢性神经系统疾病，具有重大影响。由于脑电图（EEG）的复杂性，传统诊断方法和机器学习面临挑战。深度学习（DL）通过脑电图处理和脑成像分析在癫痫管理中显示出潜力。本研究旨在通过对深度学习在癫痫中的应用进行可视化文献计量分析，填补该领域缺失的文献计量分析空白，揭示研究趋势、热点和前沿发展，以指导未来的研究方向。
在本研究中，通过科学网核心合集数据库对2006年至2025年发表的仅限英文的原创研究文章和综述进行了全面检索。使用CiteSpace、VOSviewer和Bibliometrix进行文献计量分析和可视化。
该检索从科学网核心合集数据库中检索到1266篇与深度学习应用于癫痫相关的论文，呈持续上升趋势。这些论文来自290个国家/地区的1957个组织，主要来自中国和美国。“生物医学信号处理与控制”是发表论文最多的期刊。义安理工学院的阿查里亚·乌·拉金德拉是最具权威性的作者。基于深度学习的癫痫发作检测、预测和癫痫管理是关键研究热点。此外，多模态数据整合方法正受到越来越多的关注。
本研究创新性地采用文献计量方法对深度学习在癫痫中的应用研究进行可视化分析，揭示了该领域研究的上升趋势，并确定了关键热点，如深度学习在癫痫发作检测和预测中的应用。强调了深度学习在改善癫痫管理方面的潜力，特别是在提高癫痫发作检测和预测的准确性方面，从而提高患者的生活质量。此外，研究结果突出了提高癫痫发作预测准确性、探索多模态数据整合以及为未来研究开发更具可解释性的深度学习模型的重要性。
