基于MIC心率变异性特征选择的情感识别研究  

Research on emotion recognition based on feature selection of heart rate variability by MIC algorithm

作　　者：陈瑞娟[1] 邓光华 刁小飞 孙智慧[1] 王慧泉[1] Chen Ruijuan;Deng Guanghua;Diao Xiaofei;Sun Zhihui;Wang Huiquan(School of Life Sciences,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津工业大学生命科学学院,天津300387

出　　处：《电子测量与仪器学报》2020年第12期57-65,共9页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(81901789);国家重点研发计划课题(2017YFC0806402);天津市科技计划项目(18ZXRHSY00200)资助。

摘　　要：心率变异性分析能够在情感识别中发挥重要作用,为了建立心电与情感类别之间的精准模型,提出了基于最大信息系数(maximal information coefficient,MIC)的特征选择方法。使用Aubt数据库和设计情感诱发实验进行研究,首先提取了心率变异性时域、频域、非线性及时频域40个特征参数,然后基于MIC方法结合支持向量机、随机森林、K近邻算法进行情感建模。结果显示,基于MIC特征选择方法,使用Aubt数据库针对唤醒度、效价、4类情感的分类准确度分别为90%、89%、84%,并进一步选用皮尔森相关系数、ANOVA特征选择方法与MIC进行对比;诱发实验数据中的多种一对一情感识别率均高于75%。结果表明基于MIC特征选择方法能够显著提高分类准确度,对基于心电信号进行情感识别具有重要意义。Heart rate variability analysis can play an important role in emotion recognition.In order to establish an accurate model between ECG and emotion categories,a feature selection method based on maximum information coefficient(MIC)is proposed.In this paper,the Aubt database and the design of emotional induction experiments are used for research.First,40 features based on heart rate variability in time domain,frequency domain,nonlinear and time-frequency domain were extracted,then emotion modelingwas conducted based on the MIC method combined with support vector machine,random forest and K nearest neighbor algorithm.The results show that based on the MIC feature selection method,the classification accuracy of the Aubt database for arousal,valence,and four emotions is 90%,89%,and 84%,respectively.And further choose Pearson correlation coefficient,ANOVA feature selection method to compare with MIC.In the induced experimental data,the correct classification rate ofmultiple one-to-one emotion recognition is higher than 75%.It shows that the MIC feature selection method can significantly improve the classification accuracy,which is of great significance for emotion recognition based on ECG signals.

关 键 词：心率变异性 情感识别 最大信息系数 特征选择 

分 类 号：R318[医药卫生—生物医学工程] TN98[医药卫生—基础医学]