癫痫被认为是一种结构和功能网络障碍,影响全球约5000万人。正确的疾病诊断可以导致更快的医疗行动,防止不良影响。本文报道了在第一次发作发作后的患者中用于癫痫诊断的分类器的设计,使用脑电图(EEG)记录。该数据集包括来自629名患者的静息状态脑电图,其中504项保留用于研究。该患者队列存在于291例癫痫患者和213例其他病理患者中。将数据分成两组:80%训练集和20%测试集。从脑电图中提取的特征包括功能连通性测量,图形度量,频带功率和大脑不对称比率。执行了特征缩减,模型使用机器学习(ML)技术进行训练。使用受试者工作特征曲线下面积(AUC)进行模型评估。当特别关注局灶性病变癫痫患者时,取得了较好的结果。此分类任务使用5倍交叉验证进行了优化,其中使用PCA进行特征减少的SVM实现了0.730±0.030的AUC。在测试集中,相同的模型获得了0.649的AUC。验证的减少是由队列中病理的相当大的多样性证明的。对测试模型中选定特征的分析表明,功能连通性及其图形度量具有最可观的预测能力,以及基于全频谱频率的特征。最后,提出的算法,有了一些改进,对于医生在疑似首次癫痫发作后从脑电图记录诊断癫痫具有附加价值。
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient\'s cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models\' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.