利用高频脑电的局灶性癫痫患者癫痫发作检测  

Seizure Detection in Focal Epilepsy Patients Using High Frequency EEG Signal

作　　者：王栋 李扩[2] 刘晓芳[2] 闫相国[1] 王刚[1] 

机构地区：[1]西安交通大学生物医学信息工程教育部重点实验室,西安710049 [2]西安交通大学第一附属医院神经外科,西安710061

出　　处：《西安交通大学学报》2018年第2期148-154,共7页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金资助项目(31571000;81201162;61471291);陕西省自然科学基础研究计划资助项目(2013JQ8007);中央高校基本科研业务费专项资金资助项目(xjj2017122)

摘　　要：针对现有的大多数癫痫发作自动检测方法都是在脑电的低频段进行而忽略高频成分这一现象,利用长时程头皮脑电的高频成分对局灶性癫痫患者进行癫痫发作检测。首先将19通道的脑电数据在一个滑动时间窗内利用小波分解提取出高频γ波段,再利用有向传递函数算法来提取信息流特征,求得流出信息强度特征用以降维,然后将此波段的特征通过支持向量机进行分类,通过五重交叉验证得到癫痫发作效果评价。结果表明:利用高频检测脑电癫痫发作的正确率为98.4%,平均选择性为60.7%,平均敏感性为93.4%,平均特异性为98.4%,平均检出率为95.9%;通过和使用其他子频带进行癫痫发作检测的结果对比发现,γ波段有着更高的分类效果;表明了对于局灶性癫痫患者,在癫痫发作时,其γ波段的流出信息强度显著性集中和增强在某些脑区。研究内容验证了癫痫发作与脑电中高频成分有关的观点。Aiming at the phenomenon that the most existing methods of automatic seizures detection are used to low frequency EEG and ignored the high-frequency components,we attempt to adopt the high frequency component of long term scalp EEG to detect seizures in focal epilepsy patients in this research.Gamma band is extracted in 19 channel EEG using discrete wavelet transform in a sliding window,and the information flow characteristics of each band is evaluated with directional transfer function algorithm. The intensity characteristics of the outgoing information are used to reduce the dimensions.The features are classified by support vector machine(SVM).Five-fold cross validation indicates that the proposed strategy achieves an excellent performance with the average accuracy of 98.4%,the average selectivity of 60.7%,the average sensitivity of 93.4%,the average specificity of 98.4% and the average detection rate of95.9%,and the gamma band is endowed with higher classification effect.For patients with focalepilepsy,the intensity of the gamma band outflow during seizure attack is significantly concentrated and enhanced in some brain regions.Simultaneously it validates the point that seizure attack is related to high frequency components in the literatures.

关 键 词：癫痫发作 有向传递函数 信息流差异 γ波段 支持向量机 

分 类 号：R318.04[医药卫生—生物医学工程]