基于心电脉搏特征的视觉疲劳状态识别

摘要

为从生物医学信号角度检测和评估视觉疲劳,模拟VDT作业环境,对35位健康被试者进行1.5 h的VDT疲劳实验.使用MP425数据采集卡和LabVIEW构成的数据采集系统同步采集心电(ECG)和脉搏波信号,经信号预处理分析后,提取实验前后的ECG和脉搏波信号特征.研究结果表明,ECG和脉搏波信号特征在实验前后有较大变化,采用支持向量机法对实验前后的ECG脉搏组合特征进行分类,正确率可达100%.%In order to detect and evaluate visual fatigue from biomedical signal, this paper simulates Visual Display Terminal(VDT) operating environment, and tests 35 healthy subjects for 1.5 h VDT fatigue experiment.Electrocardiograph(ECG) signals and pulse wave are collected from the subjects by using the MP425 data acquisition card and LabVIEW acquisition system.ECG and pulse wave signal features are extracted by analyzing and processing before and after VDT fatigue experiment.Analysis results show that ECG and pulse wave signal features change significantly before and after VDT fatigue experiment, the accuracy rate of classification is reached 100％ by using Support Vector Machine(SVM)method and combination features of ECG and pulse wave signals.