An EEG dataset for interictal epileptiform discharge with spatial distribution information.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-07 DOI:10.1038/s41597-025-04572-1

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

{"title":"An EEG dataset for interictal epileptiform discharge with spatial distribution information.","authors":"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","doi":"10.1038/s41597-025-04572-1","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"229"},"PeriodicalIF":6.9000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11805897/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04572-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

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.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

具有空间分布信息的癫痫样放电间歇期脑电图数据集。

癫痫样间期放电（IED）及其空间分布对癫痫的诊断、分类和治疗至关重要。现有的公开数据集存在数据量不足和缺乏空间分布信息等局限性。在本文中，我们提出了一个综合的脑电图数据集，其中包含来自84例患者的带注释的间歇癫痫数据，每个患者提供20分钟的连续原始脑电图记录，总计28小时。简易爆炸装置和意识状态（清醒/睡眠）由至少三位脑电图专家精心注释。根据发生区域将ied分为5种类型：广义、额叶、颞叶、枕叶和中央顶叶。该数据集包括2,516个IED时期和22,933个非IED时期，每4秒长。我们利用该数据集开发并验证了一个基于vgg的IED检测模型，通过包含意识和/或空间分布信息来提高性能。此外，我们的数据集可作为评估和比较现有IED检测模型的可靠测试集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.