ArticleScientific data2024
Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spatiotemporal Dynamics in Prespeech Semantic Category Decoding: An Intracranial EEG Study.eNeuro · 2026Article
- Iterative multiblock framework for high frequency EEG based neurological disorder detection.Scientific reports · 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 in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.Scientific data · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and are associated with impaired memory and cognition. Despite growing interest, most studies involving IED detections rely on visual annotations or employ simple amplitude threshold approaches. Alternatively, advanced computerized detection methods are not standardized or publicly available. To address this gap, we introduce a novel dataset comprising multichannel intracranial electroencephalography (iEEG) data recorded at two medical centers during overnight sleep with IED annotations performed by expert neurologists. Utilizing these annotations to train machine learning models via a gradient-boosting algorithm, we demonstrate automated IED detection with high precision (94.4%) and sensitivity (94.3%) that can generalize across individuals and surpass performance of a leading commercial software. The dataset featuring multi-channel annotations with sub-second resolution including hippocampus and medial temporal lobe (MTL) regions is made publicly available, together with the detection algorithm, to advance research on detection methodology, epilepsy, sleep, and cognition.
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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.