ReviewMethodsX2025
Advancements in epilepsy classification: Current trends and future directions.
Review in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Modulation of Gut Microbiome and Metabolome as One of the Potential Mechanisms of Ketogenic Diet Effect in the Treatment of Epilepsy.Nutrients · 2025Review
- Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.Frontiers in neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a comprehensive survey on categorizing focal and non-focal epilepsy using Electroencephalogram (EEG) signals. It emphasizes how recent advances in machine learning and deep learning methodologies assists in overcoming the existing challenges in classification. The paper synthesizes cutting-edge techniques with the focus on the application of hybrid models that combine traditional signal processing techniques with machine learning algorithms. By highlighting key breakthroughs in the field, the paper aims to propose novel directions for improving classification precision. Furthermore, the paper delves into the challenges faced by current methods and the possible solutions. The paper concludes with the discussion on potential future research directions, especially in areas of multimodal data integration and real-time seizure prediction, and emphasizes the potential for AI-driven personalized epilepsy treatment techniques.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.