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作 者:李昕[1,2,3] 张云鹏[1,2] 李红红[1,2] 陈泽涛[1,2] 应立娟[1,2] 吴水才[3]
机构地区:[1]燕山大学生物医学工程研究所,秦皇岛066004 [2]河北省测试计量技术及仪器重点实验室,秦皇岛066004 [3]北京工业大学生命科学与生物工程学院,北京100124
出 处:《中国生物医学工程学报》2014年第1期45-50,共6页Chinese Journal of Biomedical Engineering
基 金:河北省优秀专家出国资助项目;河北省教育厅重点项目(ZD2010115)
摘 要:慢性心理压力会带来一系列的病理、生理风险,直接影响健康。有效地评估心理压力,一直是心理压力研究中的热点问题。在心理压力评估过程中,个体差异是影响评估效果的关键。本研究针对评估心理压力/非压力反应中个体差异问题,以表面肌电信号作为评估参数,以高校即将毕业的学生人群为对象,提出了一种改进的支持向量机心理压力评估算法。算法通过对样本聚类,并将聚类信息赋予支持向量机的损失函数,实现训练样本的筛选,针对筛选后出现两类样本不平衡问题,为损失函数赋予权重来降低分类器的预测倾向性,减少训练模型的误差,补偿不平衡样本数据所造成的影响。心理压力评估分类正确率由改进前的70.34%,提高到79.31%,算法运行时间由改进前的2026.5 s减少到541.3 s。结果表明,该算法可以有效地解决个体差异对于心理压力评估效果的影响,同时降低了分类器的计算复杂度,为心理压力评估中个体差异研究提供一种可行的方案。Chronic mental pressure will affect one's health directly by bring a series of pathology and physiology risks. Effective methods of evaluating psychological pressure can detect and assess real-time stress states, warning people to pay a close attention to their health. This paper is focused on the problem of individual difference in the stress evaluation process. An improved support vector machine (SVM) evaluation algorithm in automated valuation of stress/non-stress reaction was proposed based on the measurement of surface myoelectrogram signal. The ~lgorithm clustered the samples and gave the clustering information to the loss function of SVM to achieve training samples' screening. With the imbalance problem of the two kinds of samples after screening, the weight were given to the loss function to reduce classifier' s prediction tendentiousness, which decreases the error of training sample and makes up for the influence made by the unbalanced samples. The improved algorithm increased the classification accuracy from 70. 34% to 79.31% , while algorithm running time was decreased from 2026.5 s to 541.3 s. Experimental results show that the algorithm can effectively avoid the influence resulting from the individual difference on stress appraisal effect. Meanwhile the algorithm reduces the computational complexity.
关 键 词:肌电 心理压力评估 个体差异 支持向量机 权重 myoelectrogram (EMG) SUPPORT VECTOR MACHINE (SVM)
分 类 号:R318[医药卫生—生物医学工程]
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