REVIEW article
Front. Neurol.
Sec. Neuroepidemiology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1615120
This article is part of the Research TopicLeveraging Big Data Mining to Advance Neurological ResearchView all 4 articles
Artificial Intelligence in Electroencephalography Analysis for Epilepsy Diagnosis and Management
Provisionally accepted
- 1Shanxi Medical University, Taiyuan, China
- 2Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China
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Epilepsy is a prevalent chronic neurological disorder diagnosed primarily using electroencephalography (EEG), its main auxiliary diagnostic tool. However, traditional EEG interpretation relies on manual analysis, leading to high misdiagnosis rates and low efficiency. In recent years, artificial intelligence (AI) technology, particularly deep learning (DL) and machine learning (ML), has been integrated into EEG analysis, demonstrating significant potential in epilepsy detection and monitoring, as well as therapeutic evaluation. Currently, AI-EEG has two major application models in epilepsy management: supportive AI and predictive AI. Research has revealed that AI-based EEG analysis can improve the precision and efficiency of epilepsy management, with great potential in multimodal data fusion and personalized diagnosis and treatment. However, challenges such as limited model interpretability, limited data quality, and difficulties associated with clinical translation persist. Notably, AI analysis results require verification by clinicians in conjunction with multidimensional information. The focus of future research should include optimizing algorithms, improving data quality, enhancing AI transparency, and fostering interdisciplinary collaboration to promote the clinical implementation of AI-EEG in epilepsy care. In this review, the current applications, technological advancements, limitations, and future directions of AI-EEG in epilepsy management are systematically evaluated.
Keywords: Epilepsy, Electroencephalography (EEG), Artificial intelligence (AI), deep learning (DL), Machine Learning (ML), multimodal data fusion
Received: 20 Apr 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Wang and Jing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wei Jing, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, 030032, Shanxi Province, China
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