Rudolf Magnus Institute of Neuroscience, Department of Pediatric Neurology, University Medical Center Utrecht, Utrecht, The Netherlands.
PLoS One. 2013;8(4):e59764. doi: 10.1371/journal.pone.0059764. Epub 2013 Apr 2.
Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics.
METHODOLOGY/PRINCIPAL FINDINGS: Routinely performed interictal EEG recordings at first presentation of 35 children diagnosed with partial epilepsies, and of 35 children in whom the diagnosis epilepsy was excluded (control group), were used to develop the prediction model. Children with partial epilepsy were individually matched on age and gender with children from the control group. Periods of resting-state EEG, free of abnormal slowing or epileptiform activity, were selected to construct functional networks of correlated activity. We calculated multiple network characteristics previously used in functional network epilepsy studies and used these measures to build a robust, decision tree based, prediction model. Based on epileptiform EEG activity only, EEG results supported the diagnosis of with a sensitivity and specificity of 0.77 and 0.91 respectively. In contrast, the prediction model had a sensitivity of 0.96 [95% confidence interval: 0.78-1.00] and specificity of 0.95 [95% confidence interval: 0.76-1.00] in correctly differentiating patients from controls. The overall discriminative power, quantified as the area under the receiver operating characteristic curve, was 0.89, defined as an excellent model performance. The need of a multivariable network analysis to improve diagnostic accuracy was emphasized by the lack of discriminatory power using single network characteristics or EEG's power spectral density.
CONCLUSIONS/SIGNIFICANCE: Diagnostic accuracy in children with partial epilepsy is substantially improved with a model combining functional network characteristics derived from multi-channel electroencephalogram recordings. Early and accurate diagnosis is important to start necessary treatment as soon as possible and inform patients and parents on possible risks and psychosocial aspects in relation to the diagnosis.
脑电图（EEG）采集通常用于支持疑似癫痫患者阵发性事件的癫痫起源。然而，在部分癫痫患者中，发作间期脑电图通常正常。我们旨在基于脑电图功能网络特征开发一个多变量诊断预测模型。
方法/主要发现：我们使用 35 名被诊断为部分性癫痫的儿童和 35 名排除癫痫诊断的儿童（对照组）的首次发作时的常规发作间期脑电图记录来开发预测模型。对部分性癫痫患儿进行年龄和性别匹配，与对照组儿童进行匹配。选择无异常减速或癫痫样活动的静息状态 EEG 期来构建相关活动的功能网络。我们计算了之前在功能网络癫痫研究中使用的多个网络特征，并使用这些措施构建了一个稳健的、基于决策树的预测模型。仅基于癫痫样 EEG 活动，脑电图结果的敏感性和特异性分别为 0.77 和 0.91。相比之下，预测模型在正确区分患者和对照组方面的敏感性为 0.96 [95%置信区间：0.78-1.00]，特异性为 0.95 [95%置信区间：0.76-1.00]。以受试者工作特征曲线下面积表示的整体判别能力为 0.89，定义为优秀的模型性能。缺乏使用单网络特征或 EEG 功率谱密度的判别能力，强调了需要进行多变量网络分析以提高诊断准确性。
结论/意义：使用从多通道脑电图记录中得出的功能网络特征组合，可显著提高部分性癫痫儿童的诊断准确性。早期和准确的诊断对于尽快开始必要的治疗以及告知患者和家长与诊断相关的可能风险和心理社会方面非常重要。
