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首页> 外文期刊>Journal of Neurology, Neurosurgery and Psychiatry >Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering.
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Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering.

机译：基于视频脑电图分析和自动聚类的精神性非癫痫性癫痫发作的临床分类。

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BACKGROUND: Psychogenic non-epileptic seizures (PNES) or attacks consist of paroxysmal behavioural changes that resemble an epileptic seizure but are not associated with electrophysiological epileptic changes. They are caused by a psychopathological process and are primarily diagnosed on history and video-EEG. Clinical presentation comprises a wide range of symptoms and signs, which are individually neither totally specific nor sensitive, making positive diagnosis of PNES difficult. Consequently, PNES are often misdiagnosed as epilepsy. The aim of this study was to identify homogeneous groups of PNES based on specific combinations of clinical signs with a view to improving timely diagnosis. METHODS: The authors first retrospectively analysed 22 clinical signs of 145 PNES recorded by video-EEG in 52 patients and then conducted a multiple correspondence analysis and hierarchical cluster analysis. RESULTS: Five clusters of signs were identified and named according to their main clinical features: dystonic attack with primitive gestural activity (31.6%); pauci-kinetic attack with preserved responsiveness (23.4%); pseudosyncope (16.9%); hyperkinetic prolonged attack with hyperventilation and auras (11.7%); axial dystonic prolonged attack (16.4%). When several attacks were recorded in the same patient, they were automatically classified in the same subtype in 61.5% of patients. CONCLUSION: This study proposes an objective clinical classification of PNES based on automatic clustering of clinical signs observed on video-EEG. It also suggests that PNES are stereotyped in the same patient. Application of these findings could help provide an objective diagnosis of patients with PNES.
机译：背景：精神性非癫痫发作（PNES）由发作性行为改变组成，类似于癫痫发作但与电生理癫痫发作改变无关。它们是由心理病理过程引起的，主要根据病史和视频脑电图进行诊断。临床表现包括广泛的症状和体征，它们既不是完全特异性的也不是敏感的，这使得对PNES的阳性诊断变得困难。因此，PNES经常被误诊为癫痫病。这项研究的目的是根据临床体征的特定组合确定PNES的同质群体，以期改善及时诊断。方法：作者首先回顾性分析了52例患者通过video-EEG记录的145个PNES的22种临床体征，然后进行了多重对应分析和层次聚类分析。结果：根据其主要临床特征，鉴定并命名了五类体征：肌张力性发作伴原始手势活动（31.6％）;肌张力发作。保留反应性的弱动力学攻击（23.4％）;拟晕厥（16.9％）;过度换气和先兆的运动过度发作（11.7％）;轴向张力障碍长时间发作（16.4％）。当在同一位患者中记录了几次发作时，它们会自动分类为61.5％的患者亚型。结论：本研究基于视频-EEG上观察到的临床体征的自动聚类，提出了PNES的客观临床分类。这也表明PNES在同一位患者中被定型。这些发现的应用可能有助于为PNES患者提供客观的诊断。

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