From the Department of Clinical Neurophysiology (M.A.K., L.D., V.S.H., H.T., S.B.), Aarhus University Hospital, Aarhus, Denmark; Department of Neurosurgery (P.G.L.), Rikshospitalet, Oslo University Hospital, Norway; Department of Neurosurgery (S.R.), University Hospital Erlangen, Germany; Department of Neurosurgery (S.R.), University Hospital Halle (Saale), Germany; Epilepsy Center Bethel (R.S.), Mara Hospital, Bielefeld, Germany; Krembil Brain Institute (R.W.), Toronto Western Hospital, University of Toronto, Canada; Department of Biostatistics (B.M.B.), Aarhus University, Denmark; Department of Research (M.S.), BESA GmbH, Gräfelfing, Germany; Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre, Dianalund, Denmark; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.
Neurology. 2020 May 19;94(20):e2139-e2147. doi: 10.1212/WNL.0000000000009439. Epub 2020 Apr 22.
OBJECTIVE: To define and validate criteria for accurate identification of EEG interictal epileptiform discharges (IEDs) using (1) the 6 sensor space criteria proposed by the International Federation of Clinical Neurophysiology (IFCN) and (2) a novel source space method. Criteria yielding high specificity are needed because EEG over-reading is a common cause of epilepsy misdiagnosis. METHODS: Seven raters reviewed EEG sharp transients from 100 patients with and without epilepsy (diagnosed definitively by video-EEG recording of habitual events). Raters reviewed the transients, randomized, and classified them as epileptiform or nonepileptiform in 3 separate rounds: in 2, EEG was reviewed in sensor space (scoring the presence/absence of each IFCN criterion for each transient or classifying unrestricted by criteria [expert scoring]); in the other, review and classification were performed in source space. RESULTS: Cutoff values of 4 and 5 criteria in sensor space and analysis in source space provided high accuracy (91%, 88%, and 90%, respectively), similar to expert scoring (92%). Two methods had specificity exceeding the desired threshold of 95%: using 5 IFCN criteria as cutoff and analysis in source space (both 95.65%); the sensitivity of these methods was 81.48% and 85.19%, respectively. CONCLUSIONS: The presence of 5 IFCN criteria in sensor space and analysis in source space are optimal for clinical implementation. By extracting these objective features, diagnostic accuracy similar to expert scorings is achieved. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that IFCN criteria in sensor space and analysis in source space have high specificity (>95%) and sensitivity (81%-85%) for identification of IEDs.
目的:利用(1)国际临床神经生理学联合会(IFCN)提出的 6 传感器空间标准和(2)一种新的源空间方法,定义并验证准确识别 EEG 发作间期癫痫样放电(IEDs)的标准。由于 EEG 过度解读是癫痫误诊的常见原因,因此需要高特异性的标准。
方法:7 名评分者回顾了 100 名癫痫患者和非癫痫患者(通过习惯性事件的视频-EEG 记录明确诊断)的 EEG 尖波瞬态。评分者在 3 个单独的轮次中随机回顾瞬态并将其分类为癫痫样或非癫痫样:在 2 个轮次中,在传感器空间中回顾 EEG(对每个瞬态存在/不存在每个 IFCN 标准进行评分,或不受标准限制进行分类[专家评分]);在另一个轮次中,在源空间中进行回顾和分类。
结果:在传感器空间中使用 4 个和 5 个标准的截止值以及源空间分析提供了高准确性(分别为 91%、88%和 90%),与专家评分相似(92%)。两种方法的特异性均超过期望的 95%阈值:使用 5 个 IFCN 标准作为截止值和源空间分析(均为 95.65%);这些方法的敏感性分别为 81.48%和 85.19%。
结论:在传感器空间中存在 5 个 IFCN 标准和源空间分析是临床实施的最佳选择。通过提取这些客观特征,可以实现与专家评分相似的诊断准确性。
证据分类:本研究提供了 III 级证据,表明 IFCN 标准在传感器空间和源空间分析具有高特异性(>95%)和敏感性(81%-85%),可用于识别 IEDs。
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