【摘要】 利用长时程脑电图检测癫痫发作是临床中较为广泛的应用，然而这项工作乏味、耗时，且很大程度上依赖于临床医生的自身经验和主观判断，准确性和可重复性也较低。针对长时程脑电图检测癫痫中存在的问题，提出一种基于自适应多尺度脑功能连接的癫痫发作检测方法(AMBFC),并选取10例局灶性癫痫患者的发作期和非发作期的样本作为研究对象。首先在一个滑动时间窗内，通过多元经验模态分解(MEMD)提取19通道脑电信号的7个本征模函数(IMF)分量及残差；然后建立多变量自回归(MVAR)模型，利用有向传递函数(DTF)提取流出信息强度，进行特征组合，并通过主成分分析(PCA)降维保留原始特征数目的85%;最后经代价敏感支持向量机(CSVM)分类区分发作期和非发作期脑电，并通过五重交叉验证进行癫痫发作检测算法的效果评价。结果表明，AMBFC算法检测脑电癫痫发作得到的平均准确率为98.6%,精确率为81.9%,召回率为81.4%,F2值为0.80。与各IMF分量、DTF-CSVM算法等检测结果相比，AMBFC算法更具有优越性。有望应用于长时程脑电的实时监测。更多还原

【Abstract】 Long-term EEG has been widely used to detect epileptic seizures in clinical practice. However, this approach is tedious and time-consuming, and largely depends on clinicians′ experience and subjective judgment. As a result, the accuracy and repeatability of the manual detection results are low. In this study, with the aim of solving the problem by using long-term EEG to monitor epileptic seizures, we proposed a novel adaptive and multiscale brain functional connectivity(AMBFC) method for epilepsy detection. Samples of 10 epilepsy patients during the seizure and non-seizure periods were selected as the research subjects. First, within a sliding time window, seven IMF components and residuals of the 19-channel EEG signal were extracted by MEMD. Then MVAR model was established to extract the outflow information intensity by the directional transfer function, and the features were combined and dimensionally reduced using PCA. Finally, the CSVM model was used to classify the seizure phase and non-seizure phase EEG, and the effect of epileptic seizures was evaluated through five-fold cross-validation. The results showed that the average accuracy rate of AMBFC algorithm for detecting EEG seizures was 98.6%, accuracy rate was 81.9%, recall rate was 81.4%, and F-measure value was 0.80. Compared with the detection results of the different IMF components, DTF-CSVM algorithm and methods in recent literatures, AMBFC algorithm was better. Except for the high accuracy, the proposed algorithm also achieved high precision, recall and F2 value. In conclusion, this method can be applied to real-time monitoring of long-term EEG.更多还原