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

IF 5.8 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

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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.

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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.