教育生物学杂志2026，Vol.14Issue(2)：81-85,90,6.DOI:10.3969/j.issn.2095-4301.2026.02.001
基于脑电图相位功能连接的孤独症谱系障碍识别
Identification of autism spectrum disorder based on electroencephalogram phase functional connectivity
摘要
Abstract
Objective To improve the diagnostic accuracy and specificity of autism spectrum disorder(ASD)based on the analysis of electroencephalogram(EEG)phase functional connectivity.Methods A total of 25 children with ASD(ASD group)and 25 typically developing children(control group)were recruited as participants,and their EEG data were collected.After preprocessing the EEG data,the δ(1 to 4 Hz),θ(4 to 8 Hz),α(8 to 13 Hz)and β(13 to 30 Hz)bands as well as their ASD-related characteristics were extracted.Two network properties(NP),and six-dimensional features of the spatial pattern of network topology(SPN)of the EEG connectivity map,and two-dimensional features including power spectral density(PSD)and sample entropy(SampEn)were calculated.Six classifiers based on machine learning algorithms were trained for classification experiments.The average performance metrics(accuracy,sensitivity,and specificity)of NP,SPN,PSD,and SampEn across the 4 EEG bands were computed to verify the classification efficacy of the extracted features for ASD.Results SPN achieved the optimal classification performance in the β band,and the stability of the classification results was significantly improved after merging all features.Analysis of low-proportion training data(5 participants selected from both the ASD group and the control group)showed that the model trained with merged features yielded the highest accuracy(83.33%),sensitivity(93.33%),and specificity(76.67%).Visualization analysis indicated that the functional connectivity characteristics of the ASD group and the control group differed significantly in the θ,β,and δ bands,which was conducive to the diagnostic identification of ASD.Conclusion The extraction based on EEG phase connectivity has a certain effect on ASD classification,and can serve as a potential electrophysiological indicator for ASD diagnostic identification.关键词
孤独症谱系障碍/脑电图/诊断/机器学习Key words
autism spectrum disorder/electroencephalogram/diagnosis/machine learning引用本文复制引用
官朵,张腾,唐廷贤,胡巧,代英,蒋鑫龙,钟敏..基于脑电图相位功能连接的孤独症谱系障碍识别[J].教育生物学杂志,2026,14(2):81-85,90,6.基金项目
国家重点研发计划(2023YFC3604802) （2023YFC3604802）
