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首页> 外文期刊>Journal of Neuroscience Methods >Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis
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Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis

机译：脑电癫痫样瞬态检测的标准化数据库开发：EEGnet评分系统和机器学习分析

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The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification. ? 2012.
机译：常规头皮脑电图（rsEEG）是最常见的临床神经生理学程序。 rsEEG的最重要作用是以癫痫样瞬变（ET）的形式检测癫痫的证据，也称为尖峰或尖波放电。由于ET的形态多种多样，并且与正常背景活动的一部分伪影和波相似，因此ET检测的任务很困难，并且经常犯错误。脑电中ET的可靠计算机检测技术的发展可以帮助医生解释rsEEG。我们报告了开发用于测试和培训ET检测算法的标准化数据库的进展。我们描述了一个新版本的EEGnet软件系统，用于收集有关EEG数据集的专家意见，这是一个完全基于Web浏览器的系统。我们报告了由11名获得董事会认证的学术临床神经生理学家组成的小组的脑电图评分结果，这些注释对30 s进行了注释，但100名不同患者的rsEEG记录除外。得分者的得分间信度中等，得分手间信度低至中等。为了测量此标准化rsEEG数据库的最佳大小，我们使用机器学习模型将数据库中的阵发性EEG活动分类为ET和非ET类。根据我们的结果，似乎我们的数据库将需要大于其当前大小。此外，我们的非参数分类器，即人工神经网络，比我们的参数贝叶斯分类器表现更好。在我们的特征集中，小波特征集被证明对分类最有用。 ？ 2012。

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