BACKGROUND Multiple classification systems for psychogenic nonepileptic seizures (PNES) based on semiological features have been described. We sought to compare the efficiency of four PNES classification systems. METHODS We retrospectively analysed medical and video-electroencephalography (VEEG) records of patients with PNES with at least one typical event recorded on VEEG. Semiology of PNES events was stringently classified using Hubsch, Dhiman, Wadwekar, and Asadi-Pooya's classification systems. RESULTS We studied 248 patients with PNES (78% females, mean age 23.1 ± 10.3 years) and reviewed 498 PNES events. Using Hubsch's scheme, we classified events into: dystonic attacks with primitive gestural activity (5.2%), paucikinetic attacks with preserved responsiveness (9.7%), pseudosyncope (59.8%), hyperkinetic prolonged attacks (16.2%) and axial dystonic prolonged attacks (1.6%), and unclassified (7.5%). Using Dhiman's classification, events were: abnormal motor (hypermotor [10.4%]/ partial motor [12.7%]), dialeptic type (58.6%), mixed patterns (17.3%), and unclassified (1%). Using Wadwekar's classification: dystonic attacks with primitive gestural activity (5.2%), paucikinetic attacks with preserved responsiveness (9.6%), pseudosyncope with/without hyperventilation (65.1%), hyperkinetic prolonged attacks involving limbs ± trunk (18.5%), and axial dystonic prolonged attacks (1.6%). Using Asadi-Pooya's classification, events were: hypermotor (30.1%), non-motor (62.9%), and mixed (7.0%). All events could be classified via Wadwekar and Asadi-Pooya systems. CONCLUSION In our study, pseudosyncope/ dialeptic/ non-motor semiology emerged as most frequent. Most of our patients with PNES had stereotyped semiology. All events could be classified using the schemes by Asadi-Pooya and Wadweker et al. Dhiman et al. scheme could classify 99% and 7.5% remained unclassified using Hubsch et al. scheme.

中文翻译：

---

基于视频脑电图 (VEEG) 的心因性非癫痫发作 (PNES) 符号学分类与比较分析

背景技术已经描述了基于符号学特征的心因性非癫痫发作（PNES）的多种分类系统。我们试图比较四种 PNES 分类系统的效率。方法我们回顾性分析了 PNES 患者的医学和视频脑电图 (VEEG) 记录，VEEG 记录了至少一个典型事件。PNES 事件的符号学使用 Hubsch、Dhiman、Wadwekar 和 Asadi-Pooya 的分类系统进行严格分类。结果 我们研究了 248 名 PNES 患者（78% 为女性，平均年龄 23.1 ± 10.3 岁）并回顾了 498 次 PNES 事件。使用 Hubsch 的方案，我们将事件分类为：具有原始手势活动的肌张力障碍发作 (5.2%)、保留响应性的少动性发作 (9.7%)、假性晕厥 (59.8%)、多动性长时间发作 (16. 2%) 和轴向肌张力障碍延长发作 (1.6%) 和未分类 (7.5%)。使用 Dhiman 的分类，事件是：异常运动（运动过度 [10.4%]/部分运动 [12.7%]）、透析型（58.6%）、混合型（17.3%）和未分类（1%）。使用 Wadwekar 的分类：具有原始手势活动的肌张力障碍发作 (5.2%)、保留反应性的少动症发作 (9.6%)、伴有/不伴有过度换气的假性晕厥 (65.1%)、涉及四肢 ± 躯干的多动性长期发作 (18.5%) 和轴向肌张力障碍长时间发作（1.6%）。使用 Asadi-Pooya 的分类，事件为：运动过度 (30.1%)、非运动 (62.9%) 和混合 (7.0%)。所有事件都可以通过 Wadwekar 和 Asadi-Pooya 系统进行分类。结论 在我们的研究中，假性晕厥/透析/非运动符号学是最常见的。我们的大多数 PNES 患者都有刻板的符号学。所有事件都可以使用 Asadi-Pooya 和 Wadweker 等人的方案进行分类。迪曼等人。方案可以分类 99% 和 7.5% 仍然未分类使用 Hubsch 等人。方案。