基于脑电信号预发作数据段选取的癫痫发作预测  

Seizure prediction based on pre-ictal periodselection of EEG signal

作　　者：王雅静 王群[1] 李博闻 刘志文[1] 朴媛媛[2] 遇涛[2] WANG Ya-jing;WANG Qun;LI Bo-wen;LIU Zhi-wen;PIAO Yuan-yuan;YU Tao(School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China;Xuanwu Hospital,Capital Medical University,Beijing 100053,China)

机构地区：[1]北京理工大学信息与电子学院,北京100081 [2]首都医科大学宣武医院,北京100053

出　　处：《浙江大学学报（工学版）》2020年第11期2258-2265,共8页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(81771395).

摘　　要：为了提高癫痫发作预测的准确性,提出针对病患个体进行癫痫发作预测的算法,包括特征提取、预发作数据段选取、特征挑选与导联挑选.算法采用半重叠的2 s窗对每个导联分别提取时频特征和空域特征.从发作前期选择与发作间期相比具有最大线性可分性的连续10 min数据作为预发作数据段的有效正样本.算法通过弹性网进行特征挑选,再基于所选特征通过贪婪算法选择有效导联,将有效导联的有效特征输入到分类器中.将该算法在MIT公共头皮脑电数据库和宣武医院收集的数据集上进行测试,测试结果为:在MIT数据库上的击中率为95.76%,假阳性率为0.1073 h−1;在宣武医院数据集上的击中率为97.80%,假阳性率为0.0453 h−1.结果表明,该算法具有较高的击中率和较低的假阳性率.A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epileptic prediction,including feature extraction,pre-ictal period selection,feature selection and channel selection.Time-frequency features and spatial features were extracted from each channel by 2 s windows with 1 s overlap.A continuous 10 min data was selected as a valid positive sample of the pre-ictal period from segment before seizure onset,which achieved the maximum linear separability compared with the inter-ictal period.The effective features were selected by elastic net,then the selected effective features were used to select effective channels in greedy manner.The effective features of effective channels were input into classifier.The algorithm was tested on the scalp electroencephalogram(sEEG)from the MIT Physio database and the database collected in Xuanwu Hospital.The algorithm achieved a recall of 95.76%and a false positive rate of 0.1073 h−1 in MIT database,and a recall of 97.80%and a false positive rate of 0.0453 h−1 in Xuanwu Hospital database.Results show that the algorithm has high sensitivity and low false positive rate.

关 键 词：癫痫发作预测 头皮脑电图(sEEG) 患者特异性 特征挑选 预发作数据段选取 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]