In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant ( P<0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.
本文针对传统多尺度熵在多尺度化过程中信息丢失问题，提出一种加权多重多尺度熵特征提取算法。该算法在各尺度上构建了从大到小的多重数据序列，考虑多重数据序列对该尺度样本熵的贡献程度不同，计算各个序列在该尺度序列中所占比重，以此作为系数重构各尺度样本熵。相比于传统多尺度熵算法，该算法不但克服了信息丢失问题，还充分考虑了序列的相关性与对总熵值的贡献程度，减小了尺度间的波动，更能挖掘脑电信号的细节信息。基于该算法，本文分析了孤独症（ASD）儿童脑电信号特征，与样本熵、传统多尺度熵及延搁取值法多重多尺度熵算法比较，分类准确率分别提高了 23.0%、10.4% 与 6.4%。基于该算法对比分析孤独症儿童与对照组健康儿童的 19 通道脑电信号，结果表明除 FP2 通道外，其余通道的熵值均显示健康儿童略高于孤独症儿童，且 F3、F7、F8、C3、P3 通道的熵值差异具有统计学意义（ P<0.05）。本文通过对各个脑区加权多重多尺度熵进行分类，发现前颞叶区域通道（F7、F8）的分类准确率最高，表明前颞叶可以作为评估孤独症儿童脑功能状态的敏感脑区。.
Keywords: autism; electroencephalography; sample entropy; weighted multiple multiscale entropy.