Objectives. There is emerging evidence that network/computer analysis of epileptiform discharge free electroencephalograms (EEGs) can be used to detect epilepsy, improve diagnosis and resource use. Such methods are automated and can be performed on shorter recordings of EEG. We assess the evidence and its strength in the area of seizure detection from network/computer analysis of epileptiform discharge free EEG. Methods. A scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance was conducted with a literature search of Embase, Medline and PsychINFO. Predesigned inclusion/exclusion criteria were applied to selected articles. Results. The initial search found 3398 articles. After duplicate removal and screening, 591 abstracts were reviewed, 64 articles were selected and read leading to 20 articles meeting the requisite inclusion/exclusion criteria. These were 9 reports and 2 cross-sectional studies using network analysis to compare and/or classify EEG. One review of 17 reports and 10 cross-sectional studies only aimed to classify the EEGs. One cross-sectional study discussed EEG abnormalities associated with autism. Conclusions. Epileptiform discharge free EEG features derived from network/computer analysis differ significantly between people with and without epilepsy. Diagnostic algorithms report high accuracies and could be clinically useful. There is a lack of such research within the intellectual disability (ID) and/or autism populations, where epilepsy is more prevalent and there are additional diagnostic challenges.

目标。越来越多的证据表明，癫痫样放电无脑电图 (EEG) 的网络/计算机分析可用于检测癫痫、改善诊断和资源使用。这种方法是自动化的，可以在较短的 EEG 记录上执行。我们通过无癫痫样放电 EEG 的网络/计算机分析评估癫痫发作检测领域的证据及其强度。方法。通过对 Embase、Medline 和 PsychINFO 的文献搜索，使用系统评价和元分析的首选报告项目 (PRISMA) 指南进行了范围界定审查。预先设计的纳入/排除标准适用于选定的文章。结果. 初步搜索找到 3398 篇文章。经过重复删除和筛选，审查了 591 篇摘要，选择并阅读了 64 篇文章，其中 20 篇符合必要的纳入/排除标准。这些是 9 份报告和 2 份横断面研究，使用网络分析来比较和/或分类 EEG。对 17 份报告和 10 项横断面研究的一项综述仅旨在对 EEG 进行分类。一项横断面研究讨论了与自闭症相关的脑电图异常。结论. 来自网络/计算机分析的无癫痫样放电 EEG 特征在癫痫患者和非癫痫患者之间存在显着差异。诊断算法报告的准确性很高，并且可能在临床上有用。在智力障碍 (ID) 和/或自闭症人群中缺乏此类研究，在这些人群中癫痫更为普遍，并且存在额外的诊断挑战。