基于面孔加工异常的孤独症儿童识别  

Recognition of autism spectrum disorder based on face processing abnormality

作　　者：崔冬[1,2] 韩晓雅 陈贺 韩俊霞 李小俚 康健楠[5] Dong Cui;Xiaoya Han;He Chen;Junxia Han;Xiaoli Li;Jiannan Kang(School of Information Science&Engineering,Yanshan University,Qinhuangdao 066004,China;Hebei Province Key Laboratory of Information Transmission and Signal Processing,Qinhuangdao 066004,China;State Key Laboratory of Cognitive Neuroscience and Learning,Beijing Normal University,Beijing 100875,China;Beijing Key Laboratory of Learning and Cognition,School of Psychology,Capital Normal University,Beijing 100048,China;Institute of Electronic Information Engineering,Hebei University,Baoding 071000,China)

机构地区：[1]燕山大学信息科学与工程学院,秦皇岛066004 [2]河北省信息传输与信号处理重点实验室,秦皇岛066004 [3]北京师范大学认知神经科学与学习国家重点实验室,北京100875 [4]首都师范大学心理学院,北京市“学习与认知”重点实验室,北京100048 [5]河北大学电子信息工程学院,保定071000

出　　处：《科学通报》2020年第20期2128-2135,共8页Chinese Science Bulletin

基　　金：国家自然科学基金(61761166003);河北省自然科学基金(F2018203239)资助。

摘　　要：眼动追踪技术在孤独症谱系障碍的早期诊断中具有潜在的应用价值.为研究孤独症儿童对不同面孔加工的特点,应用机器学习算法对其进行自动识别,本研究选取3~6岁孤独症儿童40名和性别、年龄相匹配的正常儿童41名观看异国陌生面孔、本国陌生面孔和本国熟悉面孔,根据两组儿童眼动坐标数据,使用机器学习算法进行自动划分兴趣区、特征选择和分类,来判断不同面孔的扫描模式是否可以用于识别孤独症儿童,并从准确率、特异性、敏感性和可靠性4个方面对分类模型进行评估.结果显示,基于不同面孔扫描模式的机器学习算法可以提取足够的信息来区分孤独症和正常儿童,最大分类准确率为90.28%,对应AUC(area under the ROC curve)为0.9317.因此,眼动追踪技术结合机器学习能够为临床诊断提供辅助的评价指标.Autism spectrum disorder(ASD),a highly complex neurodevelopmental disorder with complicated causes and processes,has core symptoms including impairment of social interaction,narrow interests,and repetitive stereotypical behaviors.Many methods have been applied to the early diagnosis of ASD,one of which is eye-tracking technology.Faces are important social stimuli,and face processing is very important for social interaction.Eye movements during face processing are a source of information which can reveal characteristics of neural development.We here study eye movements during the processing of different types of faces in autistic children.We use machine learning on eye movement information to automatically identify ASD.We recruited a total of 81 children aged 3 to 6 years(40 ASD,41 age-and gender-matched typically-developing controls)to look at faces of strangers from foreign races,faces of strangers from the children’s own race,and faces of familiar people from the children’s own race.We quantized eye-fixation coordinates using K-means,partitioning face images into K=64 different cell-like regions(areas of interest).We then used the fixation coordinate frequency distribution as features,along with the minimal redundancy and maximal relevance(mRMR)method for feature selection and support vector machines(SVM)for classification of ASD versus control.Results showed that maximum classification accuracies based on foreign face,own-race stranger face,and familiar face respectively reached 78.89%(sensitivity 81.67%,specificity 74.00%),73.89%(sensitivity 70.88%,specificity 76.38%),and 79.44%(sensitivity 82.67%,specificity 79.71%).The areas under the receiver operating characteristic curves(AUC)were 0.8065,0.8217,and 0.8387,respectively.Then,we combined features from the three types of faces to obtain a total of 192 features and used the mRMR method,selecting 27 features.With these features,the maximum classification accuracy reached 90.28%(sensitivity 91.33%,specificity 86.83%),and the AUC was 0.9317.Independent sampl

关 键 词：孤独症 眼动追踪 机器学习 面孔加工 

分 类 号：R749.94[医药卫生—神经病学与精神病学] TP181[医药卫生—临床医学]