ReviewEpilepsia open2023
EEG datasets for seizure detection and prediction- A review.
Review in Epilepsia open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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.
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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.
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Who cites it
26 citing papers in PubMed, 79 citations in OpenAlex.
- Article
- Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.Interdisciplinary sciences, computational life sciences · 2026Article
- Review
- Areas of research priorities in epilepsy: A position paper of the European Reference Network for Rare and Complex Epilepsies, EpiCARE.Epilepsia open · 2026Article
- Explainable AI-Enhanced Ensemble Protocol Using Gradient-Boosted Models for Zero-False-Alarm Seizure Detection from EEG.Sensors (Basel, Switzerland) · 2026Article
- MLSPred-bench: Transforming electroencephalography (EEG) datasets into machine learning-ready epileptic seizure prediction benchmarks.MethodsX · 2025Article
- Seizure Type Classification Based on Hybrid Feature Engineering and Mutual Information Analysis Using Electroencephalogram.Entropy (Basel, Switzerland) · 2025Article
- A companion to the development of common data elements for Sudden Unexpected Death in Epilepsy (SUDEP).Epilepsia open · 2025Article
- Ordinal Spectrum: Mapping Ordinal Patterns into Frequency Domain.Entropy (Basel, Switzerland) · 2025Article
- Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Annotating neurophysiologic data at scale with optimized human input.Journal of neural engineering · 2025Article
- An algorithm for seizure detection in rodents.Epilepsia open · 2025Article
- Article
- Seizure prediction and forecasting: a scoping review.Current opinion in neurology · 2025Article
- SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.Neuroinformatics · 2025Article
- Advancing epileptic seizure recognition through bidirectional LSTM networks.Frontiers in computational neuroscience · 2025Article
- Economic aspects of prolonged home video-EEG monitoring: a simulation study.Cost effectiveness and resource allocation : C/E · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.
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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.