基于脑电主成分分析和k-最近邻的多动症儿童与正常儿童分类研究  

Classification of Children with Attention-Deficit/Hyperactivity Disorder and Typically Developing Children Based on Electroencephalogram Principal Component Analysis and k-Nearest Neighbor

作　　者：杨姣姣[1,2] 郭倩[1,2] 李文杰[1,2] 王苏弘[3] 邹凌[1,2] 

机构地区：[1]常州大学信息科学与工程学院,常州213164 [2]常州市生物医学信息技术重点实验室,常州213164 [3]苏州大学附属第三医院脑科学研究中心,常州213003

出　　处：《生物医学工程学杂志》2016年第2期232-238,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61201096);常州市科技资助项目(CE20145055);江苏省"青蓝工程"资助项目

摘　　要：本文尝试通过脑电信号检测方法辅助多动症儿童进行临床个体化诊断。首先基于一种经典的干扰控制试验任务Simon-spatial Stroop范例采集14名多动症儿童和16名正常儿童的脑电数据,并完成滤波、分段、去伪迹等预处理;然后采用主成分分析（PCA）进行电极优化选择,分别选取每种刺激模式下出现率90%以上的优化电极作为共有电极,并提取共有电极潜伏期（200~450ms）波幅的均值特征;最后采用基于欧氏距离的k-最近邻（KNN）和基于径向基核函数的支持向量机（SVM）分类器来分类。实验发现同种试验任务中多动症儿童比正常儿童表现出更低的反应正确率和更长的反应时间;多动症儿童与正常儿童的前额叶优化电极均出现N2,顶枕叶均有P2出现,且多动症儿童的峰值更低;在该实验中KNN分类准确率高于SVM分类器,StI刺激模式下KNN分类器的最高分类准确率为89.29%。以上结果说明,干扰控制试验中多动症儿童与正常儿童的前额叶及顶枕叶的脑电信号存在差异,该结果可为多动症个体的脑电信号临床诊断提供一定科学依据。This paper aims to assist the individual clinical diagnosis of children with attention-deficit/hyperactivity disorder using electroencephalogram signal detection method.Firstly,in our experiments,we obtained and studied the electroencephalogram signals from fourteen attention-deficit/hyperactivity disorder children and sixteen typically developing children during the classic interference control task of Simon-spatial Stroop,and we completed electroencephalogram data preprocessing including filtering,segmentation,removal of artifacts and so on.Secondly,we selected the subset electroencephalogram electrodes using principal component analysis（PCA）method,and we collected the common channels of the optimal electrodes which occurrence rates were more than 90%in each kind of stimulation.We then extracted the latency（200~450ms）mean amplitude features of the common electrodes.Finally,we used the k-nearest neighbor（KNN）classifier based on Euclidean distance and the support vector machine（SVM）classifier based on radial basis kernel function to classify.From the experiment,at the same kind of interference control task,the attention-deficit/hyperactivity disorder children showed lower correct response rates and longer reaction time.The N2 emerged in prefrontal cortex while P2 presented in the inferior parietal area when all kinds of stimuli demonstrated.Meanwhile,the children with attention-deficit/hyperactivity disorder exhibited markedly reduced N2 and P2amplitude compared to typically developing children.KNN resulted in better classification accuracy than SVM classifier,and the best classification rate was 89.29%in StI task.The results showed that the electroencephalogram signals were different in the brain regions of prefrontal cortex and inferior parietal cortex between attention-deficit/hyperactivity disorder and typically developing children during the interference control task,which provided a scientific basis for the clinical diagnosis of attention-deficit/hyperactivity disorder individuals.

关 键 词：脑电图 多动症 干扰控制 主成分分析 分类 

分 类 号：R749.94[医药卫生—神经病学与精神病学] TP391.4[医药卫生—临床医学]