Bibliometric Analysis Of Machine Learning Applications In EEG For Epileptic Seizure Diagnosis Using Biblioshiny: Trends And Conceptual Structures
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https://orcid.org/0000-0003-0819-4922
Abstract
This study examines the research landscape of machine learning applications in EEG-based epileptic seizure diagnosis through bibliometric analysis. A total of 2,805 Scopus-indexed publications (1967--2024) authored by 9,003 researchers were analysed using Biblioshiny in R-Studio to explore publication trends, influential works, collaboration networks, and thematic developments. The analysis reveals a steady annual growth rate of $1.91\%$, with a significant increase in research activity after 2015 driven by advancements in deep learning techniques. While the field benefits from an average of 5.4 co-authors per document, international collaboration remains modest at $26.2\%$ of the total output. Support vector machines (SVMs), artificial neural networks (ANNs), and convolutional neural networks (CNNs) are widely used for seizure detection. However, challenges remain, including limited dataset diversity, real-world implementation barriers, and computational demands. The study finds that research output is concentrated among a few highly cited authors and journals, with fewer contributions from resource-limited regions. The findings indicate a need for broader collaborations, diverse datasets, and evaluation metrics that reflect clinical relevance rather than solely technical performance. Future research should explore explainable AI (XAI), wearable EEG technologies, and practical machine learning integration in clinical settings to improve accessibility and reliability. Addressing these challenges can enhance the impact of machine learning in EEG-based epilepsy diagnosis, leading to better patient outcomes. This bibliometric study provides a detailed, quantified overview of the field's progress, offering insights that can guide future research towards greater inclusivity, collaboration, and real-world applicability.
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