Dauwels Justin, Cash Sydney, Westover M Brandon
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4435-8. doi: 10.1109/EMBC.2014.6944608.
Detection of interictal discharges is a key element of interpreting EEGs during the diagnosis and management of epilepsy. Because interpretation of clinical EEG data is time-intensive and reliant on experts who are in short supply, there is a great need for automated spike detectors. However, attempts to develop general-purpose spike detectors have so far been severely limited by a lack of expert-annotated data. Huge databases of interictal discharges are therefore in great demand for the development of general-purpose detectors. Detailed manual annotation of interictal discharges is time consuming, which severely limits the willingness of experts to participate. To address such problems, a graphical user interface "SpikeGUI" was developed in our work for the purposes of EEG viewing and rapid interictal discharge annotation. "SpikeGUI" substantially speeds up the task of annotating interictal discharges using a custom-built algorithm based on a combination of template matching and online machine learning techniques. While the algorithm is currently tailored to annotation of interictal epileptiform discharges, it can easily be generalized to other waveforms and signal types.
发作间期放电的检测是癫痫诊断和管理过程中解读脑电图的关键要素。由于临床脑电图数据的解读耗时且依赖于供不应求的专家，因此对自动尖峰检测器有很大需求。然而，迄今为止，开发通用尖峰检测器的尝试因缺乏专家标注数据而受到严重限制。因此，对于通用检测器的开发而言，急需大量发作间期放电数据库。发作间期放电的详细手动标注耗时，这严重限制了专家参与的意愿。为解决此类问题，我们在工作中开发了一个图形用户界面“SpikeGUI”，用于脑电图查看和发作间期放电的快速标注。“SpikeGUI”使用基于模板匹配和在线机器学习技术相结合的定制算法，大幅加快了发作间期放电的标注任务。虽然该算法目前是针对发作间期癫痫样放电的标注量身定制的，但它可以很容易地推广到其他波形和信号类型。
