Dai Xi-Jian, Xu Qiang, Hu Jianping, Zhang QiRui, Xu Yin, Zhang Zhiqiang, Lu Guangming
Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China.
Front Neurol. 2019 Nov 14;10:1201. doi: 10.3389/fneur.2019.01201. eCollection 2019.
To investigate the performance of substate classification of children with benign epilepsy with centrotemporal spikes (BECTS) by granger causality density (GCD) based support vector machine (SVM) model. Forty-two children with BECTS (21 females, 21 males; mean age, 8.6 ± 1.96 years) were classified into interictal epileptic discharges (IEDs; 11 females, 10 males) and non-IEDs (10 females, 11 males) substates depending on presence of central-temporal spikes or not. GCD was calculated on four metrics, including inflow, outflow, total-flow (inflow + outflow) and int-flow (inflow - outflow) connectivity. SVM classifier was applied to discriminate the two substates. The Rolandic area, caudate, dorsal attention network, visual cortex, language networks, and cerebellum had discriminative effect on distinguishing the two substates. Relative to each of the four GCD metrics, using combined metrics could reach up the classification performance (best value; AUC, 0.928; accuracy rate, 90.83%; sensitivity, 90%; specificity, 95%), especially for the combinations with more than three GCD metrics. Specially, combined the inflow, outflow and int-flow metric received the best classification performance with the highest AUC value, classification accuracy and specificity. Furthermore, the GCD-SVM model received good and stable classification performance across 14 dimension reduced data sets. The GCD-SVM model could be used for BECTS substate classification, which might have the potential to provide a promising model for IEDs detection. This may help assist clinicians for administer drugs and prognosis evaluation.
通过基于格兰杰因果密度（GCD）的支持向量机（SVM）模型，研究伴有中央颞区棘波的儿童良性癫痫（BECTS）的亚状态分类性能。42例BECTS患儿（21例女性，21例男性；平均年龄8.6±1.96岁）根据中央颞区棘波的有无分为发作间期癫痫样放电（IEDs；11例女性，10例男性）和非IEDs（10例女性，11例男性）亚状态。计算了包括流入、流出、总流量（流入+流出）和内流量（流入-流出）连通性在内的四个指标的GCD。应用SVM分类器区分这两个亚状态。罗兰区、尾状核、背侧注意网络、视觉皮层、语言网络和小脑对区分这两个亚状态有鉴别作用。相对于四个GCD指标中的每一个，使用组合指标可以达到更高的分类性能（最佳值；AUC，0.928；准确率，90.83%；敏感性，90%；特异性，95%），特别是对于三个以上GCD指标的组合。特别地，将流入、流出和内流量指标组合起来，获得了最佳的分类性能，具有最高的AUC值、分类准确率和特异性。此外，GCD-SVM模型在14个降维数据集上均获得了良好且稳定的分类性能。GCD-SVM模型可用于BECTS亚状态分类，这可能为IEDs检测提供一个有前景的模型。这可能有助于协助临床医生进行药物管理和预后评估。
