Study of Stress Rules Based on HRV Features
Study of Stress Rules Based on HRV Features
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基于HRV特征的应激规律研究
DOI：
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发表时间：
2018
期刊：
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通讯作者：
Yan
中科院分区：
文献类型：
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作者：
Gang Zheng;Yingli Wang;Yan
本文提出了一种基于改进决策树的心率变异性特征值范围估计方法，用于描述应激状态。该方法从应激刺激实验得到的短时（2min）心电信号中提取HRV特征。然后利用决策树的白盒分类特性，将多棵决策树归纳为一棵具有通用识别性能的树。最后，从树中提取HRV特征的范围。本文提出的方法可以显示的HRV特征的范围，并已被实验用250心电信号从114名受试者。结果表明，利用心率变异性特征的变化范围，可以识别放松和高应激两种不同的状态，识别准确率为86.7%，不低于传统的分类模型。而且识别过程简单，实际应用价值高。
In this paper, we proposed an estimation method of the ranges of HRV feature values based on improved decision tree, which used to describe stress. This method extracted HRV features from short-time (2min) ECG signals that obtained from stress-stimulate experiments. Then, multiple decision trees were summarized as a tree with universal recognition performance by utilizing the white-box classification characteristic of the decision tree. Finally, the ranges of the HRV features were extracted from the tree. The method proposed in this paper can display the ranges of the HRV features and had been experimented with 250 ECG signals collected from 114 subjects. The results show, two different states of relaxation and high-stress can be recognized by utilizing the ranges of the HRV features, and the recognition accuracy rate was 86.7%, not less than the traditional classification models. Moreover, the recognition process is simple and the practical application value is high.