基于多维复杂度的精神分裂症脑磁信号区分

Multidimensional complexity measures for MEG signal classification of schizophrenia

为了更有效地识别脑磁信号,提出一种基于多维复杂度的脑磁信号分类方法。首先提取信号的AR模型系数、频带能量、近似熵和Lempel-Ziv复杂度作为特征。然后运用增L减R搜索算法结合距离准则选择通道。最后采用遗传算法选择特征子集,分别运用BP神经网络和SVM分类器检测特征子集的性能并对信号分类。实验结果表明精神分裂症患者的近似熵和Lempel-Ziv复杂度都高于正常人,患者的脑磁信号可能更加复杂。增L减R搜索算法选择的通道大多分布在颞叶区,即颞叶区域的通道可能携带了更多的差异信息。采用BP神经网络和SVM对特征数据分类,分别得到了98.5%和99.75%的正确率。

In order to classify the MEG signal more efficiently, an approach based on multidimensional complexity is proposed for MEG signal classification. First, several features including Autoregressive（AR）model parameters, band power, approximate entropy, Lempel-Ziv complexity are extracted from MEG signals. Then, plus-L minus-R（LRS）techniques combined with distance principle are employed to select informative channels. After channel selection, the best features are selected using Genetic Algorithm（GA）, classifiers including BP neural networks and Support Vector Machine（SVM） are used to classify the reduced feature set of the two groups. The results show that the approximate entropy and Lempel-Ziv complexity of schizophrenic are higher, it is suggested that the MEG signal is more complex. The interesting point is that most of selected channels are located in the temporal lobes, it means that the selected channels in the temporal lobes carry more discriminative information. A classification accuracy of 98.5% and 99.75% is obtained by BP neural networks and SVM respectively.