摘要
提取分析孤独症谱系障碍(ASD)患者脑电(EEG)信号特征对疾病的诊断治疗具有重要意义。本研究基于递归定量分析(RQA)方法探索ASD儿童和正常发育(TD)儿童EEG信号非线性特征差异。运用RQA方法提取受试者各脑区EEG信号递归率(RR)、确定性(DET)、平均对角线长度(LADL)非线性特征,并结合支持向量机对ASD儿童和TD儿童进行分类。研究结果表明,对于全脑区(包括:顶叶、额叶、枕叶、颞叶),当选取RR、DET、LADL三个特征组合时,得到84%的最大分类准确率,对应敏感性为76%,特异性为92%,曲线下面积(AUC)值为0.875;对于顶额叶区(包括:顶叶、额叶),当RR、DET、LADL三个特征组合时,得到最大分类准确率为82%,对应敏感性为72%,特异性为92%,AUC值为0.781。研究结果表明,RQA方法提取EEG信号的RR、DET、LADL特征能成为区分ASD儿童和TD儿童的客观指标,并结合机器学习方法能为ASD临床诊断提供辅助评价指标,同时,ASD儿童和TD儿童EEG信号的RR、DET、LADL特征差异在顶额叶区具有统计学意义,本研究根据脑区所承担的功能来分析ASD儿童临床特征,为今后的诊断和治疗提供了参考。
Extraction and analysis of electroencephalogram(EEG)signal characteristics of patients with autism spectrum disorder(ASD)is of great significance for the diagnosis and treatment of the disease.Based on recurrence quantitative analysis(RQA)method,this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development(TD).In the experiment,RQA method was used to extract nonlinear features such as recurrence rate(RR),determinism(DET)and length of average diagonal line(LADL)of EEG signals in different brain regions of subjects,and support vector machine was combined to classify children with ASD and TD.The research results show that for the whole brain area(including parietal lobe,frontal lobe,occipital lobe and temporal lobe),when the three feature combinations of RR,DET and LADL are selected,the maximum classification accuracy rate is 84%,the sensitivity is 76%,the specificity is 92%,and the corresponding area under the curve(AUC)value is 0.875.For parietal lobe and frontal lobe(including parietal lobe,frontal lobe),when the three features of RR,DET and LADL are combined,the maximum classification accuracy rate is 82%,the sensitivity is 72%,and the specificity is 92%,which corresponds to an AUC value of 0.781.The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD,and combined with machine learning methods,the method can provide auxiliary evaluation indicators for clinical diagnosis.At the same time,the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe.This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions,and provides help for future diagnosis and treatment.
作者
赵杰
张志明
万灵燕
李小俚
康健楠
ZHAO Jie;ZHANG Zhiming;WAN Lingyan;LI Xiaoli;KANG Jiannan(Institute of Electronic Information Engineering,Hebei University,Baoding,Hebei 071000,P.R.China;Machine Vision Technology Innovation Center of Hebei Province,Baoding,Hebei 071000,P.R.China;State Key Laboratory of Cognitive Neuroscience and Learning,Beijing Normal University,Beijing 100875,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2021年第4期663-670,共8页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(62001153,61761166003)。
关键词
孤独症
脑电图
递归图
递归定量分析
autism
electroencephalogram
recurrence plot
recurrence quantification analysis
