There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.
临床常用人眼识别癫痫颅内脑电异常放电致痫灶（EZ）定位的方式具有一定局限性，而目前通过提取颅内脑电特征进行致痫区定位的准确率也有待进一步提高。神经脆弱性作为一种运用动态网络模型的新方法，在癫痫致痫区定位方面表现出潜在的应用价值。本文采用神经脆弱性分析方法对20名患者35次癫痫发作的立体定向脑电图信号进行了分析，进而利用随机森林模型对致痫灶电极进行了分类，并与通过短时傅里叶变换提取的六个不同频段时频特征的分类结果进行了比较。结果显示，基于时频分析的致痫灶电极工作特征曲线下面积（AUC）介于0.870（δ频段）至0.956（高γ频段）之间，且曲线下面积随频段频率的升高而升高，而利用神经脆弱性进行分类的AUC可达0.957，且将神经脆弱性融合γ及高γ频段时频特性进行分类后AUC可进一步提升至0.969。本文验证了神经脆弱性在识别癫痫致痫病灶方面的有效性，为进一步的临床应用提供了理论参考。.
Keywords: Epilepsy; Neural fragility; Random forest; Time-frequency analysis.