基于奇异谱分析方法的孤独症儿童脑电信号特征提取及分类

Feature extraction and classification of EEG signals in autistic children based on singular spectrum analysis

孤独症谱系障碍是一种复杂的神经发育障碍,其早期发现和精确诊断非常重要.脑电图由于其具有较高的时间分辨率,是一种常用的神经成像技术.本文提出奇异谱分析方法对脑电信号进行伪迹去除和节律提取,通过从正常儿童和孤独症儿童脑电信号中提取统一α节律AFu,求其能量并使用支持向量机方法进行分类比较结果;进一步,使用加权重心点探究个体化α峰值频率PFA及节律AFi,使用SSA方法提取AFi后重复上述进行分类比较.结果显示,未经SSA处理的脑电数据提取AFu相对能量作为特征,分类准确度为81.36%,而使用SSA预处理后,分类准确度提升至89.83%,验证了SSA方法伪迹去除的有效性;使用个体化α节律AFi相对能量作为特征,SVM分类准确率降低至81.36%,而将个体化α节律AFi相对能量和α峰值频率PAF作为共同特征可得到94.92%的分类准确率.此结果揭示孤独症儿童脑电节律异常体现为两个方面:频段分布和功率调制,即孤独症儿童α节律出现了低频偏移及相对能量的降低.本研究从方法验证及病理揭示的角度为孤独症儿童的辅助诊断提供了有力的技术手段和科学依据.

Autism spectrum disorder is a heterogeneous neurodevelopmental disorder involving social, emotional, cognitive, and behavioral disorders. In recent years, with the increasing number of patients with autism, the autistic population has received more and more attention abroad, but the pathogenesis is still unclear. The clinical diagnosis depends on the behavior observation and scale evaluation. Therefore, objective indicators are of great significance for the diagnosis of autism. Electroencephalogram(EEG) is a commonly used neuroimaging technique for its high temporal resolution. This study mainly foucus on the feature extraction of EEG and classification of children with autism based on the analyses of EEG, including the individualized α peak frequency(PAF) and the relative energy of AFi. In this study, we enrolled 30 children with autism(3-6 years old) and 29 age-matched normal children. Resting-state EEG was obtained from each subject with eyes open in a quiet environment. Singular spectrum analysis(SSA) method is proposed to remove artifacts and extract rhythm from EEG signals. The unified α rhythm AFu, was extracted from the EEG signals of normal and autistic children and the classification results were compared by support vector machine(SVM) method. Furthermore, the weighted centroid point was used to explore the individualized α peak frequency(PAF) and rhythmic AFi. The AFi was extracted by SSA method and then repeated and compared. The results showed that the relative energy of AFu was extracted from the EEG data without preprocessing by SSA;the classification accuracy was 81.36%. After preprocessing by SSA, the classification accuracy was increased to 89.83%, which verified the validity of SSA for artifact removing. Using individual the relative energy of α rhythm AFi as the feature, the classification accuracy is reduced to 81.36%, but the classification accuracy rate of 94.92% could be obtained by using individual the relative energy of α rhythm AFi and α peak frequency as common features. The results showed that the abnormal EEG rhythm in autistic children is reflected in two aspects: frequency distribution and power modulation;the α rhythm of autistic children has a low frequency offset and a decrease in relative energy. This study provides a powerful technical means and scientific basis for the auxiliary diagnosis of autistic children from the point of method verification and pathological revealing.