Automatic Depression Discrimination on FNIRS by Using FastICA/WPD and SVM

摘要：

　　A method is proposed for distinguishing patients with depression from normal controls based on data measured by FNIRS during a cognitive task.First,Fast Independent Component Analysis(FastICA)and Wavelet Package Decomposition(WPD)are used to extract features from 52-channel Functional Near-Infrared Spectroscopy(FNIRS)data of patients with depression and normal healthy persons.Then a classifier based on Support Vector Machine(SVM)is designed for classification.The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy 86.7647%for total and 90.74%for patients.Also,the results suggested that FNIRS may be a promising clinical tool in the diagnosis and treatment of psychiatric disorders.

关键词： Depression discrimination FNIRS FastICA WPD SVM

作者: Hong Song Weilong Du Qingjie Zhao

作者单位: School of Software,Beijing Institute of Technology,Beijing 100081,China School of Computer Science,Beijing Institute of Technology,Beijing 100081,China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 257-265

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）