Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
Epilepsia. 2013 Oct;54(10):1743-52. doi: 10.1111/epi.12339. Epub 2013 Aug 14.
Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model.
Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy (STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average (LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision.
Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval (CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones.
Source localization of rhythmic ictal activity using a distributed source model (LAURA) for the ictal EEG signals selected with a standardized method is feasible in clinical practice and has a good diagnostic accuracy. Our findings encourage clinical neurophysiologists assessing ictal EEGs to include this method in their armamentarium.
尽管准确识别癫痫发作起始区是术前评估的重要组成部分，但对癫痫发作期脑电图（EEG）信号的源定位关注较少。我们的研究旨在使用分布式源模型估计节律性癫痫发作期 EEG 活动源定位的准确性。
对符合纳入标准的 42 例连续病例进行节律性癫痫发作头皮 EEG 活动的源定位。该研究是根据诊断准确性研究的建议（STARD）设计的。初始癫痫发作 EEG 信号是使用基于癫痫发作活动的频率分析和电压分布的标准化方法选择的。使用分布式源模型-局部自回归平均（LAURA）进行源定位。根据多学科癫痫手术团队的共识结论确定了敏感性、特异性和一致性（kappa）的测量。基于手术患者的手术结果计算了预测值。为了估计癫痫发作源分析的临床价值，我们比较了一致和不一致结果的似然比。源定位是在对临床数据和手术决策进行盲法处理后进行的。
参考标准可用于 33 例患者。癫痫发作源定位的敏感性为 70%，特异性为 76%。平均一致性（kappa）测量值为 0.61，对应于高度一致（95%置信区间（CI）为 0.38-0.84）。20 例患者接受了切除术。无癫痫发作的阳性预测值（PPV）为 92%，阴性预测值（NPV）为 43%。与不一致的结果相比，一致结果的可能性比高 9 倍。
使用标准化方法选择的癫痫发作 EEG 信号的分布式源模型（LAURA）进行节律性癫痫发作活动的源定位在临床实践中是可行的，具有良好的诊断准确性。我们的研究结果鼓励评估癫痫发作 EEG 的临床神经生理学家在其武器库中纳入这种方法。
