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首页> 外文期刊>Neural Network World >USING GENETIC PROGRAMMING TO SELECT THE INFORMATIVE EEG-BASED FEATURES TO DISTINGUISH SCHIZOPHRENIC PATIENTS
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USING GENETIC PROGRAMMING TO SELECT THE INFORMATIVE EEG-BASED FEATURES TO DISTINGUISH SCHIZOPHRENIC PATIENTS

机译：使用遗传程序选择基于信息脑电的特征来区分精神分裂症患者

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摘要

There is growing interest to analyze electroencephalogram (EEG) sig-nals with the objective of classifying schizophrenic patients from the control sub-jects. In this study. EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocess-ing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of ap-plying the LDA. MLDA and Adaboost respectively provided 78%. 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remark-ably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
机译：为了从控制对象中对精神分裂症患者进行分类，分析脑电图（EEG）信号的兴趣日益浓厚。在这个研究中。使用二十个表面电极记录15位精神分裂症患者和19位年龄匹配的对照对象的EEG信号。在预处理阶段之后，从其记录信号中提取了包括自回归（AR）模型系数，带功率和分形维数在内的几个特征。实施了三个分类器，包括线性判别分析（LDA），多LDA（MLDA）和自适应增强（Adaboost），以对精神分裂症和正常受试者的脑电特征进行分类。在培训阶段，最后在测试阶段，进行留一（参与者）交叉验证。应用LDA的结果。 MLDA和Adaboost分别提供了78％。两组之间的分类准确度分别为81％和82％。为了进一步改进，采用了遗传编程（GP）来选择更多信息功能并删除多余的功能。在将GP应用于特征向量后，结果得到了显着改善，因此使用LDA，MLDA和Adaboost分类器的两组的分类率分别产生82％，84％和93％的准确性。

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  • 来源
    《Neural Network World》 |2012年第1期|p.3-20|共18页
  • 作者

    Malihe Sabeti; Reza Doostani; Toktam Zoughi;

  • 作者单位

    Department of CSE & IT, Faculty of Electrical and Computer Engineering, Shiraz University,Shiraz, Iran;

    Department of CSE & IT, Faculty of Electrical and Computer Engineering, Shiraz University,Shiraz, Iran;

    Department of CSE & IT, Faculty of Electrical and Computer Engineering, Shiraz University,Shiraz, Iran;

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  • 正文语种 eng
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  • 关键词

    features selection; GP; adaboost; LDA; MLDA; schizophrenic; EEG;

    机译：特征选择;GP;adaboost;LDA;MLDA;精神分裂症脑电图;

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