基于脑电图（EEG）和眼动追踪数据，采用支持向量机（SVM）方法识别自闭症谱系障碍儿童。

The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data.

作者信息

Kang Jiannan, Han Xiaoya, Song Jiajia, Niu Zikang, Li Xiaoli

机构信息

College of Electronic & Information Engineering, Hebei University, Baoding, China.

School of Information Science & Engineering, Yanshan University, Qinhuangdao, China.

出版信息

Comput Biol Med. 2020 May;120:103722. doi: 10.1016/j.compbiomed.2020.103722. Epub 2020 Mar 23.

DOI:10.1016/j.compbiomed.2020.103722

PMID:32250854

Abstract

OBJECTIVE

To identify autistic children, we used features extracted from two modalities (EEG and eye-tracking) as input to a machine learning approach (SVM).

METHODS

A total of 97 children aged from 3 to 6 were enrolled in the present study. After resting-state EEG data recording, the children performed eye-tracking tests individually on own-race and other-race stranger faces stimuli. Power spectrum analysis was used for EEG analysis and areas of interest (AOI) were selected for face gaze analysis of eye-tracking data. The minimum redundancy maximum relevance (MRMR) feature selection method combined with SVM classifiers were used for classification of autistic versus typically developing children.

RESULTS

Results showed that classification accuracy from combining two types of data reached a maximum of 85.44%, with AUC = 0.93, when 32 features were selected.

LIMITATIONS

The sample consisted of children aged from 3 to 6, and no younger patients were included.

CONCLUSIONS

Our machine learning approach, combining EEG and eye-tracking data, may be a useful tool for the identification of children with ASD, and may help for diagnostic processes.

摘要

目的

为了识别自闭症儿童，我们使用从两种模式（脑电图和眼动追踪）提取的特征作为机器学习方法（支持向量机）的输入。

方法

本研究共纳入97名3至6岁的儿童。在静息状态脑电图数据记录后，孩子们分别对自己种族和其他种族陌生人面孔刺激进行眼动追踪测试。功率谱分析用于脑电图分析，感兴趣区域（AOI）被选择用于眼动追踪数据的面部注视分析。最小冗余最大相关性（MRMR）特征选择方法与支持向量机分类器相结合用于自闭症儿童与正常发育儿童的分类。