An EEG dataset for interictal epileptiform discharge with spatial distribution information
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...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04572-1 |
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| Summary: | 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. |
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| ISSN: | 2052-4463 |