Suppr超能文献

癫痫发作库:一个可供分析的癫痫发作信号数据存储库。

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

作者信息

Li Xiaojin, Huang Yan, Tao Shiqiang, Cui Licong, Lhatoo Samden D, Zhang Guo-Qiang

机构信息

University of Texas Health Science Center at Houston, Houston, TX 77030.

Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40506.

出版信息

AMIA Annu Symp Proc. 2020 Mar 4;2019:1111-1120. eCollection 2019.

Abstract

Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the data for developing computational methods) seizure signal datasets with associated tools for seizure data management and visualization. We introduce SeizureBank, a web-based data management and visualization system for epileptic seizures. SeizureBank comes with a built-in seizure data preparation pipeline and web-based interfaces for querying, exporting and visualizing seizure-related signal data. In this pilot study, 224 seizures from 115 patients were extracted from over one terabyte of signal data and deposited in SeizureBank. To demonstrate the value of this approach, we develop a feature-based seizure identification approach and evaluate the performance on a variety of data sources. The results can serve as a cross-dataset evaluation benchmark for future seizure identification studies.

摘要

全球约有6000万人患有癫痫发作。癫痫研究的机器学习方法面临的一个关键挑战是缺乏可供分析的(在使用数据开发计算方法时无需额外预处理)癫痫发作信号数据集的数据资源以及癫痫发作数据管理和可视化的相关工具。我们推出了SeizureBank,这是一个基于网络的癫痫发作数据管理和可视化系统。SeizureBank附带了一个内置的癫痫发作数据准备管道以及用于查询、导出和可视化癫痫发作相关信号数据的网络界面。在这项初步研究中,从超过1太字节的信号数据中提取了115名患者的224次癫痫发作,并存入了SeizureBank。为了证明这种方法的价值,我们开发了一种基于特征的癫痫发作识别方法,并在各种数据源上评估其性能。这些结果可作为未来癫痫发作识别研究的跨数据集评估基准。

相似文献

4
The detection of epileptic seizure signals based on fuzzy entropy.基于模糊熵的癫痫发作信号检测
J Neurosci Methods. 2015 Mar 30;243:18-25. doi: 10.1016/j.jneumeth.2015.01.015. Epub 2015 Jan 19.
5
Epileptic seizure detection in EEG signal using machine learning techniques.使用机器学习技术检测脑电图(EEG)信号中的癫痫发作
Australas Phys Eng Sci Med. 2018 Mar;41(1):81-94. doi: 10.1007/s13246-017-0610-y. Epub 2017 Dec 20.
9
Adaptive Seizure Onset Detection Framework Using a Hybrid PCA-CSP Approach.基于 PCA-CSP 混合方法的自适应癫痫发作起始检测框架。
IEEE J Biomed Health Inform. 2018 Jan;22(1):154-160. doi: 10.1109/JBHI.2017.2703873. Epub 2017 May 12.
10
Parameter pattern discovery in nonlinear dynamic model for EEGs analysis.脑电图分析非线性动力学模型中的参数模式发现
J Integr Neurosci. 2016 Sep;15(3):381-402. doi: 10.1142/S0219635216500242. Epub 2016 Oct 24.

本文引用的文献

2
HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring.HyCLASSS:一种用于自动睡眠阶段评分的混合分类器。
IEEE J Biomed Health Inform. 2018 Mar;22(2):375-385. doi: 10.1109/JBHI.2017.2668993. Epub 2017 Feb 17.
5
A rule-based automatic sleep staging method.基于规则的自动睡眠分期方法。
J Neurosci Methods. 2012 Mar 30;205(1):169-76. doi: 10.1016/j.jneumeth.2011.12.022. Epub 2012 Jan 9.
6
D³: Data-Driven Documents.D³:数据驱动文档。
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2301-9. doi: 10.1109/TVCG.2011.185.
10
EEG analysis based on time domain properties.基于时域特性的脑电图分析。
Electroencephalogr Clin Neurophysiol. 1970 Sep;29(3):306-10. doi: 10.1016/0013-4694(70)90143-4.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验