ArticleScientific data2025
An EEG dataset for interictal epileptiform discharge with spatial distribution information.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis.PloS one · 2026Article
- Artificial intelligence for automated detection of interictal epileptiform discharges: methodological progress, clinical evaluation, and neurodevelopmental context.Frontiers in cell and developmental biology · 2026Review
- Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.Epilepsia · 2025Article
- An EEG dataset for interictal epileptiform discharge with spatial distribution information.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interictal epileptiform discharge (IED) and its spatial distribution are critical for the diagnosis, classification, and treatment of epilepsy. Existing publicly available datasets suffer from limitations such as insufficient data amount and lack of spatial distribution information. In this paper, we present a comprehensive EEG dataset containing annotated interictal epileptic data from 84 patients, each contributing 20 minutes of continuous raw EEG recordings, totaling 28 hours. IEDs and states of consciousness (wake/sleep) were meticulously annotated by at least three EEG experts. The IEDs were categorized into five types based on occurrence regions: generalized, frontal, temporal, occipital, and centro-parietal. The dataset includes 2,516 IED epochs and 22,933 non-IED epochs, each 4 seconds long. We developed and validated a VGG-based model for IED detection using this dataset, achieving improved performance with the inclusion of consciousness and/or spatial distribution information. Additionally, our dataset serves as a reliable test set for evaluating and comparing existing IED detection models.
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.