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文章基本信息

  • 标题:Using non-linear features of EEG for ADHD/normal participants’ classification
  • 本地全文:下载
  • 作者:Farnaz Ghassemi ; Farnaz Ghassemi ; Mohammad Hassan_Moradi
  • 期刊名称:Procedia - Social and Behavioral Sciences
  • 印刷版ISSN:1877-0428
  • 出版年度:2012
  • 卷号:32
  • 页码:148-152
  • DOI:10.1016/j.sbspro.2012.01.024
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis study investigates the non-linear features of electroencephalogram signals regarding ADHD and normal adult participants while performing Continuous Performance Test. Three non-linear features were extracted from the EEG signals. ADHD and age-matched normal groups were investigated separately which revealed that there is a significant relation between clinical presentation of the participants and some non-linear features. The accuracy of 88% and 96% were achieved in classification of clinical and non-clinical participants using one and two features respectively. The best classification result was obtained with a combination of two features in Wavelet-Entropy group.
  • 关键词:Attention-Deficit Hyperactivity Disorder (ADHD);K-nearest neighbors classifier;Continuous Performance Test (CPT);electroencephalogram;feature extraction;sustained attention
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