基于小波变化和随机森林的心理采集装置信号处理研究

Signal processing based on wavelet change and random forest

为准确有效地评估大学生心理健康状态,研究通过使用心理采集装置收集大学生的生物信号数据,并利用小波变换和随机森林方法对信号进行处理和分析。对该心理信号处理方法进行有消息验证,发现其对焦虑和抑郁的分类评估误差为6.7%,运行时间为2.7 s,其评估性能及效率较其他方法更优。故研究利用该方法对某高校大学生心理状况进行评估,发现大四学生中焦虑、抑郁、焦虑合并抑郁的比例为3.5%,4.5%和3.5%,较其他年级更高。综上结果可知,大四学生的心理压力更大,大学教育机构应该加强对大四学生的心理健康支持和关怀,并帮助学生提高应对压力和情绪管理的能力。

In order to accurately and effectively evaluate the mental health status of college students,the study collected the biological signal data by using the mental acquisition device,and processed and analyzed the signal by using the wavelet transform and random forest method.The mental signal processing method was verified and found that the classification evaluation error of anxiety and depression was 6.7%and the running time was 2.7 s,so the evaluation performance and efficiency were better than other methods.Therefore,the study used this method to evaluate the psychological status of a college student,and found that the proportion of anxiety,depression,anxiety and depression was 3.5%,4.5%and 3.5%,which were higher than that of other grades.In conclusion,the results show that senior students’psychological pressure is greater,and university educational institutions should strengthen the mental health support and care for senior students,and help students improve their ability to cope with stress and emotional management.