基于心电的铁路列车驾驶压力检测研究  

Test of railway train drivers'stress by using ECG signal

作　　者：刘坤 焦钰博 张晓明 陈晓宇 蒋朝哲[1] LIU Kun;JIAO Yubo;ZHANG Xiaoming;CHEN Xiaoyu;JIANG Chaozhe(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu Sichuan 610031,China)

机构地区：[1]西南交通大学交通运输与物流学院,四川成都610031

出　　处：《中国安全科学学报》2022年第6期31-37,共7页China Safety Science Journal

基　　金：国家自然科学基金资助(71871188);国家社会科学基金资助(15GBL143)。

摘　　要：为实时检测列车司机压力水平,基于高铁驾驶行为与安全仿真平台,获取16名被试不同驾驶速度下的主观压力量表和心电(ECG)信号。首先,通过分析主观压力量表,探究不同速度下司机的压力水平;其次,统计分析不同压力水平下的心率变异性(HRV)特征;最后,比较最邻近算法(KNN),支持向量机(SVM),随机森林(RF)3种机器学习算法在压力检测方面的表现,并分析不同输入特征对分类器性能的影响。研究表明:随着速度的增加,司机的压力增大。连续R波之间的时间差间隔大于50 ms的数量(NN50),连续R波之间的时间差间隔大于50 ms的数量占比(PNN50),低频段功率值与高频段功率值之比(LF/HF),心脏交感神经指数(CSI)在不同的压力条件下具有显著性差异。在高速条件下,PNN50、HF、LF/HF减小,NN50增加,其中LF/HF值显著降低。此外,特征选择和特征标准化均有助于提高模型的表现;相比KNN和RF,径向基为核函数(RBF)的SVM分类器模型在检测驾驶压力方面的效果最好,准确度为71.2%。In order to test stress level of railway train drivers in real time,stress response inventory and ECG signal data of 16 subjects under different train speeds were collected by using high-speed rail driving behavior and safety simulation platform.Firstly,stress response inventory was analyzed to investigate drivers'stress level along with increasing train speed.Then,HRV features were statistically analyzed at different stress levels.Finally,K nearest neighbor(KNN)algorithm,support vector machine(SVM),and random forest(RF)were compared in testing stress level,and influence of different input characteristics on these classifiers'performance was analyzed.The results show that drivers'pressure will rise along with the increase of speed,and there are significant differences for number of successive normal to normal intervals pairs that differ more than 50 ms(NN50),proportion of number of successive normal to normal intervals more than 50ms(PNN50),ratio of low frequency and high frequency(LF/HF)and cardiac sympathetic index(CSI)between different driving speeds.Besides,PNN50,HF,and LF/HF decrease with increased driving speed,while NN50 increase.In particular,LF/HF decrease significantly.Moreover,feature selection and feature normalization could improve the model's accuracy,and best performance is achieved by SVM classifier model with radial basis function(RBF)as kernel function,with an accuracy of 71.2%.

关 键 词：心电(ECG)信号 驾驶压力 列车司机 驾驶速度 机器学习 

分 类 号：X912[环境科学与工程—安全科学]