摘要
基于EGI公司64导脑电采集系统,采集了16位青少年抑郁症患者和16位正常人静息态下闭眼4分钟的脑电数据。运用频谱不对称分析法(Spectral Asymmetry Index,SASI)和去趋势波动分析(Detrended Fluctuation Analysis,DFA)算法提取脑电时域和频域特征。针对提取的特征的导联,一方面,选择最佳电极Pz作为分类的导联,另一方面,通过遗传算法对所有导联进行筛选,将筛选后的导联特征用于分类。使用支持向量机(Support Vector Machine,SVM)在单导联和多导联的情况下,对抑郁症患者和正常人进行分类,结果发现,单导联下,使用SVM分类器对抑郁组和对照组的SASI和DFA结果进行分类,分类精度分别为45.5%和51.5%,使用遗传算法的分类精度分别为78.1%和90.6%,SASI算法的计算实时性优于DFA算法,DFA算法的准确性优于SASI算法。该研究为抑郁症的计算机辅助诊断提供了理论依据。
Based on the 64-channel EEG acquisition system of EGI Company,the EEG data of 16 adolescent patients with depression and 16 healthy patients with eyes closed for 4 minutes at resting-state data are collected.Secondly,Spectral Asymmetry Index(SASI)and Detrended Fluctuation Analysis(DFA)algorithm are applied to extract EEG timefrequency domain features.For the extracted features from the channel,on the hand,the features from the single optimal electrode Pz are selected as the channel of classification;on the other hand,the features from the multi-channels selected by Genetic Algorithm(GA)are used for classification.Finally,the Support Vector Machine(SVM)is applied to a single channel and multi-channels to classify depression patients and healthy people.The classification accuracies of the single channel are 45.5%and 51.5%,respectively,while the classification accuracies of the multi-channels selected by GA are 78.1%and 90.6%,respectively.Experimental results show that SASI algorithm is better than that of DFA real-time algorithm of computing,and the DFA algorithm accuracy is better than SASI algorithm.This study provides a theoretical basis for computer aided diagnosis of depression.
作者
沈潇童
毕卉
王苏弘
李文杰
邹凌
SHEN Xiaotong;BI Hui;WANG Suhong;LI Wenjie;ZOU Ling(School of Information Science&Engineering,Changzhou University,Changzhou,Jiangsu 213164,China;Changzhou Key Laboratory of Biomedical Information Technology,Changzhou,Jiangsu 213164,China;The First People’s Hospital of Changzhou,Changzhou,Jiangsu 213003,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第22期154-159,共6页
Computer Engineering and Applications
基金
江苏省科技厅社会发展项目(No.BE2018638)
常州市科技项目(No.CE20195025)
江苏省“333高层次人才培养工程”项目
常州大学科研资助项目(No.ZMF18020322)。
