Artificial Intelligence Applied to Epilepsy: A Bibliometric Analysis of Machine Learning Techniques for Early Seizure Detection

Epilepsy, one of the most common neurological disorders worldwide, significantly affects the quality of life of millions of people due to its unpredictable seizure episodes. Early detection of seizures becomes a critical area to improve the diagnosis, treatment, and monitoring of the disease. This study conducts a systematic review and a detailed bibliometric analysis of recent research focused on the use of advanced machine learning (ML) techniques for the early detection of epileptic seizures. This study analyzes relevant scientific literature, obtained through an exhaustive search in the Scopus database, covering the period 2020-2024. The PRISMA methodology was applied to ensure transparency and rigor at each phase of the process. The results show a significant increase in scientific production on this topic, highlighting the predominant use of hybrid techniques that combine convolutional neural networks (CNNs), support vector machines (SVMs), and geometric deep learning methods, demonstrating high effectiveness in real clinical settings. In addition, a growing trend was identified toward the use of wearable devices and multimodal analysis, offering innovative and highly accurate solutions for real-time early alerts. The research also revealed persistent challenges in effective clinical implementation, such as interindividual variability, limited availability of labeled data, and the need for robust validation under real-world conditions.