Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.

中文翻译：

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通过融合功能性和有效脑电图网络的常见空间模式来识别无结构异常的难治性癫痫


药物难治性癫痫（RE）被认为与结构性病变有关，但一些 RE 患者在常规磁共振成像扫描中未显示出明显的结构异常（RE-no-SA）。由于大多数药物控制性癫痫（MCE）患者也没有表现出结构异常，因此需要制定可靠的评估来区分 RE-no-SA 患者和 MCE 患者，以避免误诊和不适当的治疗。使用静息态头皮脑电图 (EEG) 数据集，我们从 RE-no-SA 患者和 MCE 患者的功能有效的脑电图网络中提取网络空间模式 (SPN) 特征。与传统静息态脑电网络特性的表现相比，SPN特征在对这两组癫痫患者进行分类时表现出显着的优越性，功能有效的脑电网络的SPN特征的准确率分别为90.00%和80.00% ， 分别。通过进一步融合功能网络和有效网络的 SPN 特征，我们证明可以达到 96.67% 的最高准确度，灵敏度为 100%，特异性为 92.86%。总的来说，这些发现不仅表明融合的功能性和有效的 SPN 特征有望作为区分 RE-no-SA 患者和 MCE 患者的可靠测量方法，而且还可能为探索难治性癫痫的复杂神经生理学提供新的视角。