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Semi-automated annotation of signal events in clinical EEG data

机译:临床脑电图数据中的信号事件的半自动注释

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To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus. The algorithm is designed to annotate six types of EEG events. Two model training schemes, namely threshold-based and volume-based, are evaluated. In the threshold-based scheme the threshold of confidence scores is optimized in the initial training iteration, whereas for the volume-based scheme only a certain amount of data is preserved after each iteration. Recognition performance is improved 2% absolute and the system is capable of automatically annotating previously unlabeled data. Given that the interpretation of clinical EEG data is an exceedingly difficult task, this study provides some evidence that the proposed method is a viable alternative to expensive manual annotation.
机译:为了有效,最先进的机器学习技术需要大量的注释数据。医疗保健中有许多引人注目的应用,可以从深度学习技术提供的高性能自动决策支持系统中受益,但它们缺乏应用复杂机器学习模型所需的全面数据资源。此外,出于经济原因,非常困难地为这些应用程序创建大型注释语料。因此,自动注释技术变得越来越重要。在这项研究中,我们调查了使用主动学习算法自动注释大EEG语料库的有效性。该算法旨在注释六种类型的EEG事件。评估两种模型训练方案,即基于阈值和基于批量的。在基于阈值的方案中,置信度分数的阈值在初始训练迭代中优化,而基于卷的方案,则仅在每次迭代之后仅保留一定量的数据。识别性能提高了2%绝对,系统能够自动注释先前未标记的数据。鉴于临床脑电图数据的解释是一个非常困难的任务,本研究提供了一些证据表明,所提出的方法是昂贵的手动注释的可行替代方案。

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