ReviewBrain sciences2025
Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.
Review in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches to predict seizure-onset zones in patients with drug-resistant epilepsy: a systematic review.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Towards Interpretable Seizure Detection: An Excitation/Inhibition Dynamic Polynomial Network Framework for Electroencephalography.Sensors (Basel, Switzerland) · 2026Article
- Enhanced smart commuting with artificial intelligence for intelligent health and safety monitoring in school buses.Scientific reports · 2026Article
- Computational modeling of seizure reconstruction and attenuation via model reference control.Scientific reports · 2026Article
- WaveNet's Precision in iEEG Classification.Neuroinformatics · 2026Article
- TSCL-LwF: A Cross-Subject Emotion Recognition Model via Multi-Scale CNN and Incremental Learning Strategy.Brain sciences · 2026Article
- Generalization or mirage? Data leakage and reported performance in neonatal EEG seizure detection models: a systematic review.BioData mining · 2026Review
- Research on epilepsy detection methods based on interpretable features and machine learning.PloS one · 2026Article
- Digital Tools for Seizure Monitoring and Self-Management in Epilepsy: A Narrative Review.Journal of clinical medicine · 2025Review
- A Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection.Entropy (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesEpileptic seizures significantly impact patients' lives due to their unpredictability, making early and accurate detection crucial for effective treatment. Machine learning (ML) models based on electroencephalogram (EEG) signals have been explored for automated seizure detection. This meta-analysis reviews the performance of ML models in seizure detection and analyzes factors such as the model type (deep learning vs. traditional ML), data preprocessing methods, and dataset types.
aimThis study aims to provide an evidence-based foundation for the future development of intelligent tools by evaluating the performance of ML models in detecting epileptic seizures through a meta-analysis.
methodsA systematic search of multiple databases up to April 2025 identified 60 studies and 93 datasets. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated using Stata 17.0. Subgroup analyses were performed to identify sources of heterogeneity. Publication bias was assessed using Deek's test and funnel plots.
resultsThe pooled sensitivity, specificity, and AUC were 0.96 (95% CI 0.95-0.97), 0.97 (95% CI 0.96-0.98), and 0.99 (95% CI 0.98-1.00), respectively, indicating a good performance of ML in seizure detection. Subgroup analyses revealed that the model type, data preprocessing methods, and dataset type contributed to heterogeneity.
conclusionsML shows a strong potential for EEG-based seizure detection. Imaging devices integrating ML may serve as effective tools for early epilepsy diagnosis. However, larger, multicenter clinical studies are needed to validate these algorithms and enhance their interpretability, safety, and applicability in real-world clinical settings.
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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.