基于心理痛苦和静息态脑电成分的多模态数据对抑郁症自杀未遂的分类效能  

Classification Model of Suicide Attempt based on Psychological Pain and Rest-state EEG Signals in Patients with Major Depressive Disorder

作　　者：安依雯 李杨 李欢欢[1] 温晓通 郭婷[1] 王湘[2] AN Yi-wen;LI Yang;LI Huan-huan;WEN Xiao-tong;GUO Ting;WANG Xiang(Department of Psychology,Renmin University of China,Beijing 100872,China;Medical Psychological Center,The Second Xiangya Hospital,Central South University,Changsha 410011,China)

机构地区：[1]中国人民大学心理学系,北京100872 [2]中南大学湘雅二医院医学心理研究中心,长沙410011

出　　处：《中国临床心理学杂志》2024年第1期1-8,共8页Chinese Journal of Clinical Psychology

基　　金：中央高校专项中国人民大学科学研究基金面上重大项目(NO:21XNL016)。

摘　　要：目的:考察结合心理痛苦三因素模型和静息态脑电成分构建重性抑郁症患者自杀未遂分类模型的区分效能和重要特征集。方法:采用便利取样,选取73名重性抑郁症患者和36名健康对照组进行临床量表评估和静息态脑电数据采集。结合人口统计学指标、临床量表得分和静息态脑电指标,采用机器学习的支持向量机算法,构建自杀未遂多模态分类模型。结果:(1)自杀未遂多模态分类模型的准确率为85.01%,AUC为0.93;仅采用静息态脑电指标构建的自杀未遂单模态分类模型准确率为72.10%,AUC为0.59;(2)痛苦逃避是自杀未遂多模态分类模型的首位重要特征,以下依次为年龄、oz通道的低频gamma和抑郁;(3)高频和低频gamma是痛苦逃避分类模型和自杀未遂多模态分类模型中共同的重要特征。结论:自杀未遂多模态分类模型区分效能优良,痛苦逃避是优于抑郁的区分有无自杀未遂的最重要行为特征。gamma作为与痛苦逃避密切相关的脑电特征,其活动异常可能是自杀未遂的神经基础。Objective:To examine the efficiency of suicide attempt classification model and the ranks of pain avoidance and the rest-state EEG signals in the optimal feature set by using machine learning technique.Methods:Seventy-three patients with major depressive disorder and 36 healthy controls were selected by convenience sampling for clinical evaluation and rest-state EEG data collection.Then through fusion of demographic feature,clinical scores and rest-state EEG feature,and based on the support vector machine algorithm for machine learning,the suicide classification model was constructed.Results:(1)The accuracy of the multimodal classification model for suicide attempts was 85.01%with an AUC of 0.93;the accuracy of the unimodal classification model for suicide attempts constructed using only rest-state EEG indicators was72.10%with an AUC of 0.59;(2)The important feature set of multimodal classification models of suicide attempts were:distress avoidance,age,low-frequency gamma of the oz channel and depression;(3)The low-frequency and high-frequency gamma were common important features in both the pain avoidance classification model and the multimodal classification model of suicide attempts.Conclusion:The efficacy of suicide attempts multimodal classification model is excellent.Pain avoidance is the best behavioral feature distinguishing depressed patients with suicide attempts and it's better than depression.As an EEG feature closely related to pain avoidance,the abnormal of gamma may be the neural basis of suicide attempts.

关 键 词：痛苦逃避 自杀未遂 机器学习 静息态脑电 

分 类 号：R395.1[哲学宗教—心理学]