Deep learning applied in epilepsy: Bibliometric and visual analysis

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.

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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251375840
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep learning applied in epilepsy: Bibliometric and visual analysis

Wu Yiman, Wu Wenqi
Digital Health
EEG and Brain-Computer Interfaces
article

Deep learning applied in epilepsy: Bibliometric and visual analysis

Wu Yiman, Wu Wenqi
article en

Abstract

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.

Digital HealthVol. 11
Wenzhou University (CN), Jiangsu Vocational College of Medicine (CN), Wenzhou University of Technology
Openalex Percentile: Top 76%
EEG and Brain-Computer Interfaces
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