轻度抑郁症脑电特征分析与机器识别研究

Study on Analysis and Recognition of EEG Characteristics of Mild Depression

轻度抑郁症的快速筛查和识别具有重要现实意义.采用静息态脑电信号探索了轻度抑郁症机器识别的有效方法,旨在找出与抑郁关系密切的脑电特性以及分类算法.首先,对各个导联的原始脑电数据加时间窗分段,计算抑郁症患者和正常人的脑电活动性、移动性、复杂度;然后,采用Burg算法和小波变换,分别提取每段脑电信号的频域特征和时频非线性特征;最后,利用支持向量机算法进行抑郁脑电分类,分析不同时间窗口、导联组合、特征组合、节律组合以及机器学习算法对识别结果的影响.实验结果表明:使用支持向量机分类器,选用20 s时间窗口,在O2、T5导联组合,以及活跃度、移动性、小波能量熵、小波奇异熵特征组合和脑电alpha、beta、gamma节律组合下,得到抑郁识别准确率94.24%、召回率92.35%、精确度96.23%的最佳分类结果.

The rapid screening and identification of mild depression have important practical significance.The thesis uses resting state EEG signals to explore effective methods of machine recognition for mild depression,aiming to find out the EEG characteristics and classification algorithms that are closely related to depression.First,add time window segments to the original EEG data of each link to calculate the EEG activity,mobility,and complexity of both depression patients and normal people;then use Burg algorithm and wavelet transform separately to extract frequency domain features and time-frequency non-linear features from each EEG signal;finally,the support vector machine(SVM)algorithm was used to classify depression EEG,and the effects of different time Windows,lead combinations,feature combinations,rhythm combinations and machine learning algorithms on the recognition results were analyzed.The experimental results show that the depression can be identified with accuracy rate of 94.24%,recall rate of 92.35%,and precision rate of 96.23%,by using a support vector machine classifier,selecting a 20 s time window,the combination of O2 and T5 leads,as well as the combination of activity,mobility,wavelet energy entropy,wavelet singular entropy feature and EEG alpha,beta,gamma rhythm combination.