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癫痫症数据库 - 一个欧洲癫痫数据库。

EPILEPSIAE - a European epilepsy database.

机构信息

Epilepsy Center, University Hospital Freiburg, Germany.

出版信息

Comput Methods Programs Biomed. 2012 Jun;106(3):127-38. doi: 10.1016/j.cmpb.2010.08.011. Epub 2010 Sep 21.

DOI:10.1016/j.cmpb.2010.08.011
PMID:20863589
Abstract

With a worldwide prevalence of about 1%, epilepsy is one of the most common serious brain diseases with profound physical, psychological and, social consequences. Characteristic symptoms are seizures caused by abnormally synchronized neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or, consciousness. The possibility to predict the occurrence of epileptic seizures by monitoring the electroencephalographic activity (EEG) is considered one of the most promising options to establish new therapeutic strategies for the considerable fraction of patients with currently insufficiently controlled seizures. Here, a database is presented which is part of an EU-funded project "EPILEPSIAE" aiming at the development of seizure prediction algorithms which can monitor the EEG for seizure precursors. High-quality, long-term continuous EEG data, enriched with clinical metadata, which so far have not been available, are managed in this database as a joint effort of epilepsy centers in Portugal (Coimbra), France (Paris) and Germany (Freiburg). The architecture and the underlying schema are here reported for this database. It was designed for an efficient organization, access and search of the data of 300 epilepsy patients, including high quality long-term EEG recordings, obtained with scalp and intracranial electrodes, as well as derived features and supplementary clinical and imaging data. The organization of this European database will allow for accessibility by a wide spectrum of research groups and may serve as a model for similar databases planned for the future.

摘要

癫痫的全球患病率约为 1%,是最常见的严重脑部疾病之一,会对身体、心理和社会造成严重后果。其特征症状是由神经元异常同步活动引起的癫痫发作,可能导致运动功能、感知、言语、记忆或意识的暂时障碍。通过监测脑电图(EEG)来预测癫痫发作的可能性,被认为是为目前控制不佳的大量癫痫患者建立新治疗策略的最有前途的选择之一。本文介绍了一个数据库,它是欧盟资助的“EPILEPSIAE”项目的一部分,旨在开发可以监测癫痫发作前兆的癫痫预测算法。该数据库管理着高质量、长期连续的 EEG 数据,并辅以临床元数据,这些数据是葡萄牙(科英布拉)、法国(巴黎)和德国(弗莱堡)的癫痫中心共同努力的结果。本文报告了该数据库的架构和基础模式。该数据库旨在高效组织、访问和搜索 300 名癫痫患者的数据,包括使用头皮和颅内电极获得的高质量长期 EEG 记录,以及衍生特征和补充的临床及影像数据。该欧洲数据库的组织将允许广泛的研究小组访问,并可能成为未来计划类似数据库的模型。

相似文献

1
EPILEPSIAE - a European epilepsy database.癫痫症数据库 - 一个欧洲癫痫数据库。
Comput Methods Programs Biomed. 2012 Jun;106(3):127-38. doi: 10.1016/j.cmpb.2010.08.011. Epub 2010 Sep 21.
2
Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.基于计算智能技术的癫痫发作预测器:对 278 例患者的对比研究。
Comput Methods Programs Biomed. 2014 May;114(3):324-36. doi: 10.1016/j.cmpb.2014.02.007. Epub 2014 Feb 26.
3
The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients.EPILEPSIAE 数据库:一个广泛的癫痫患者脑电图数据库。
Epilepsia. 2012 Sep;53(9):1669-76. doi: 10.1111/j.1528-1167.2012.03564.x. Epub 2012 Jun 27.
4
Views of patients with epilepsy on seizure prediction devices.癫痫患者对癫痫预测设备的看法。
Epilepsy Behav. 2010 Aug;18(4):388-96. doi: 10.1016/j.yebeh.2010.05.008. Epub 2010 Jul 10.
5
Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns.检测长期人类脑电图中的癫痫发作:一种自动在线实时检测和分类多形性发作模式的新方法。
J Clin Neurophysiol. 2008 Jun;25(3):119-31. doi: 10.1097/WNP.0b013e3181775993.
6
Adaptive epileptic seizure prediction system.自适应癫痫发作预测系统。
IEEE Trans Biomed Eng. 2003 May;50(5):616-27. doi: 10.1109/TBME.2003.810689.
7
Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings.一种多变量癫痫发作检测与预测方法在非侵入性和颅内长期脑电图记录中的应用。
Clin Neurophysiol. 2008 Jan;119(1):197-211. doi: 10.1016/j.clinph.2007.09.130. Epub 2007 Nov 26.
8
Predictors of epilepsy surgery outcome: a meta-analysis.癫痫手术结果的预测因素:一项荟萃分析。
Epilepsy Res. 2004 Nov;62(1):75-87. doi: 10.1016/j.eplepsyres.2004.08.006.
9
The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods.高质量脑电图数据库在改善和评估癫痫发作预测方法中的作用。
Epilepsy Behav. 2011 Dec;22 Suppl 1:S88-93. doi: 10.1016/j.yebeh.2011.08.030.
10
Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings.新皮层癫痫发作前的时空动力学:幅度与相位耦合
IEEE Trans Biomed Eng. 2003 May;50(5):571-83. doi: 10.1109/TBME.2003.810696.

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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.基于非线性动力学和深度学习的癫痫脑电信号自动检测与预测综述
Front Neurosci. 2025 Aug 18;19:1630664. doi: 10.3389/fnins.2025.1630664. eCollection 2025.
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Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.虚拟脑电图采集:脑电图生成方法综述
Sensors (Basel). 2025 May 18;25(10):3178. doi: 10.3390/s25103178.
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Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.
犬类脑电图对人类有帮助:通过多空间对齐进行跨物种和跨模态癫痫发作检测。
Natl Sci Rev. 2025 Mar 4;12(6):nwaf086. doi: 10.1093/nsr/nwaf086. eCollection 2025 Jun.
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Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition.临界动力学可预测认知表现,并为影响认知的异质机制提供一个通用框架。
Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2417117122. doi: 10.1073/pnas.2417117122. Epub 2025 Apr 3.
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DynamoSort: Using machine learning approaches for the automatic classification of seizure dynamotypes.DynamoSort:使用机器学习方法对癫痫发作动态类型进行自动分类。
bioRxiv. 2025 Feb 17:2025.02.12.637999. doi: 10.1101/2025.02.12.637999.
6
A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages.癫痫发作分类的新视角:将发作动态类型分类法应用于无创脑电图并研究睡眠各阶段的动态变化
eNeuro. 2025 Jan 16;12(1). doi: 10.1523/ENEURO.0157-24.2024. Print 2025 Jan.
7
The Imaging Database for Epilepsy And Surgery (IDEAS).癫痫与手术影像数据库(IDEAS)。
Epilepsia. 2025 Feb;66(2):471-481. doi: 10.1111/epi.18192. Epub 2024 Dec 5.
8
A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.基于脑电图信号处理与深度学习的癫痫检测与预测方法综述
Front Neurosci. 2024 Nov 15;18:1468967. doi: 10.3389/fnins.2024.1468967. eCollection 2024.
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Artificial intelligence for brain disease diagnosis using electroencephalogram signals.基于脑电图信号的脑疾病诊断人工智能。
J Zhejiang Univ Sci B. 2024 Oct 15;25(10):914-940. doi: 10.1631/jzus.B2400103.
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Critical dynamics and interictal epileptiform discharge: a comparative analysis with respect to tracking seizure risk cycles.临界动力学与发作间期癫痫样放电:关于追踪癫痫发作风险周期的比较分析。
Front Netw Physiol. 2024 Jul 9;4:1420217. doi: 10.3389/fnetp.2024.1420217. eCollection 2024.