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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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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| author | Nan Lin Mengxuan Zheng Lian Li Peng Hu Weifang Gao Heyang Sun Chang Xu Gonglin Yuan Zi Liang Yisu Dong Haibo He Liying Cui Qiang Lu |
| author_facet | Nan Lin Mengxuan Zheng Lian Li Peng Hu Weifang Gao Heyang Sun Chang Xu Gonglin Yuan Zi Liang Yisu Dong Haibo He Liying Cui Qiang Lu |
| author_sort | Nan Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3bf6cfd8fa3b432bb43b9fc7b9a9b58b |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-3bf6cfd8fa3b432bb43b9fc7b9a9b58b2025-02-09T12:11:32ZengNature PortfolioScientific Data2052-44632025-02-011211810.1038/s41597-025-04572-1An EEG dataset for interictal epileptiform discharge with spatial distribution informationNan Lin0Mengxuan Zheng1Lian Li2Peng Hu3Weifang Gao4Heyang Sun5Chang Xu6Gonglin Yuan7Zi Liang8Yisu Dong9Haibo He10Liying Cui11Qiang Lu12Department of Neurology, Peking Union Medical College HospitalNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdDepartment of Neurology, Peking Union Medical College HospitalDepartment of Neurology, Peking Union Medical College HospitalNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdNetEase Media Technology Co., LtdDepartment of Neurology, Peking Union Medical College HospitalDepartment of Neurology, Peking Union Medical College HospitalAbstract 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.https://doi.org/10.1038/s41597-025-04572-1 |
| spellingShingle | Nan Lin Mengxuan Zheng Lian Li Peng Hu Weifang Gao Heyang Sun Chang Xu Gonglin Yuan Zi Liang Yisu Dong Haibo He Liying Cui Qiang Lu An EEG dataset for interictal epileptiform discharge with spatial distribution information Scientific Data |
| title | An EEG dataset for interictal epileptiform discharge with spatial distribution information |
| title_full | An EEG dataset for interictal epileptiform discharge with spatial distribution information |
| title_fullStr | An EEG dataset for interictal epileptiform discharge with spatial distribution information |
| title_full_unstemmed | An EEG dataset for interictal epileptiform discharge with spatial distribution information |
| title_short | An EEG dataset for interictal epileptiform discharge with spatial distribution information |
| title_sort | eeg dataset for interictal epileptiform discharge with spatial distribution information |
| url | https://doi.org/10.1038/s41597-025-04572-1 |
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