生物医学工程学杂志

生物医学工程学杂志

基于熵算法的孤独症谱系障碍儿童脑电特征提取与分类

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孤独症谱系障碍(ASD)儿童的早期诊断至关重要。脑电图(EEG)是最常用于神经成像的技术之一,其使用方便并且包含信息丰富。本文从 ASD 儿童和正常儿童的 EEG 信号中提取近似熵(ApEn)、样本熵(SaEn)、排序熵(PeEn)和小波熵(WaEn)四种熵特征,应用独立样本 t 检验分析组间差异,利用支持向量机(SVM)学习算法为不同脑区的每种熵测量建立分类模型,最后通过置换检验搜索优化子集,使 SVM 模型实现最佳性能。结果表明,与正常对照组相比,ASD 儿童脑电复杂度较低;在所有四种熵中,WaEn 的分类性能优于其他熵;分类效果在不同脑区表现出差异性,其中额叶区域表现最佳;最后经过特征选择,筛选出六个特征,建立分类模型,分类准确率最高提高到 84.55%。本研究结果可为孤独症的早期发现提供帮助。

The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's t-test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of feartures, with which the SVM model achieved best performance. The results showed that the complexity of children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six feartures were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.

关键词: 孤独症谱系障碍; 脑电图; ; 分类; 特征选择

Key words: autism spectrum disorders; electroencephalography; entropy; classification; feature selection

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