534522Eindhoven University of Technology, Eindhoven, the Netherlands.
98810Radboud University, Nijmegen, the Netherlands.
Clin EEG Neurosci. 2023 May;54(3):255-264. doi: 10.1177/15500594211050492. Epub 2021 Nov 1.
Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.
脑电图（EEG）的解读，无论是通过人工（由人类评判者）还是自动（通过计算机技术）分析，对于非惊厥性癫痫持续状态（NCSE）的诊断仍然不够可靠。本研究旨在确定脑电图分析中的典型陷阱，并就如何避免这些陷阱提出建议。我们分析了经临床确诊或疑似 NCSE 的个体的脑电图记录。通过 2 名独立的评判者对发作期间（发作放电）的癫痫样 EEG 活动进行视觉分析。我们调查了通过低评判者间一致性来量化的不可靠脑电图视觉解释是否可以通过发作放电和个体临床数据的特征来预测。此外，还通过内部算法对 EEG 记录进行自动分析。为了进一步探讨不可靠脑电图解释的原因，2 名癫痫学家根据视觉和自动分析之间的差异，分析最有可能被错误解释为发作放电的 EEG 模式。具有逐渐起始（在 3 秒内逐渐发展）的短发作放电容易被错误解释。增加 2 分钟的发作放电有助于 κ 统计量增加>0.1。其他问题包括对异常背景活动（慢波活动、其他异常脑活动和发作样运动伪影）、连续发作间期放电和连续短发作放电的错误解释。建议将 NCSE-EEG 的持续时间标准延长至 10 秒以上，这是 NCSE 工作标准中常用的标准。使用历史 EEG 知识、个体化算法和上下文相关的报警阈值也可能避免这些陷阱。
