计算机工程与应用2016，Vol.52Issue(23)：12-18,24,8.DOI:10.3778/j.issn.1002-8331.1603-0391
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 classi-fication of schizophrenia
摘要
Abstract
In order to classify the MEG signal more efficiently, an approach based on multidimensional complexity is pro-posed 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.关键词
精神分裂症/特征提取/特征选择/遗传算法/脑磁图(MEG)信号分类区分Key words
schizophrenic/feature extraction/feature selection/genetic algorithm/Magnetoencephalography(MEG)signal classification分类
医药卫生引用本文复制引用
张学军,彭丽艳,黄丽亚,成谢锋..基于多维复杂度的精神分裂症脑磁信号区分[J].计算机工程与应用,2016,52(23):12-18,24,8.基金项目
国家自然科学基金（No.61271334）。 （）
