ArticleDigital health
Deep learning applied in epilepsy: Bibliometric and visual analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.