基于小波包节律和支持向量机的警戒低觉醒脑电信号识别方法  

Recognition of Low Arousal Level Electroencephalogram in the Vigilance Based on Wavelet Packet Rhythm and Support Vector Machine

作　　者：杨建平[1,2] 张德乾[3] 罗文浪[1,2] 肖晓朋[1] 

机构地区：[1]井冈山大学电子与信息工程学院,吉安343009 [2]流域生态与地理环境监测国家测绘地理信息局重点实验室,吉安343009 [3]井冈山大学教育学院,吉安343009

出　　处：《生物医学工程学杂志》2016年第1期61-66,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(31260238);江西省自然科学基金资助项目(20151BAB207063)

摘　　要：平乏、单调的监控作业容易使作业人员觉醒水平下降,为提高监控工作的绩效,需识别及唤醒低觉醒状态,因此本文提出以脑电信号(EEG)为研究对象的低觉醒状态识别方法。运用小波包变换分解警戒作业人员的EEG信号,获取EEG信号中的δ、θ、α、β等节律成分;结合各节律计算相对能量和高低频能量比参数等特征,组成低觉醒状态识别的特征向量,并使用支持向量机对模拟警戒作业中的低觉醒状态进行了识别。实验结果显示,本文方法能够很好地区分警戒作业中的低觉醒状态和觉醒状态,识别率高。与其它分析方法相比,该方法能够有效地识别警戒作业中的低觉醒状态,能够为低觉醒状态的唤醒机制提供技术支持。Poor and monotonous work could easily lead to a decrease of arousal level of the monitoring work personnel.In order to improve the performance of monitoring work,low arousal level needs to be recognized and awakened.We proposed a recognition method of low arousal by the electroencephalogram（EEG）as the object of study to recognize the low arousal level in the vigilance.We used wavelet packet transform to decompose the EEG signal so the EEG rhythms of each component were obtained,and then we calculated the parameters of relative energy and energy ratio of high-low frequency,and constructed the feature vector to monitor low arousal state in the operation.We finally used support vector machine（SVM）to recognize the low arousal state in the simulate operation.The experimental results showed that the method introduced in this article could well distinguish low arousal level from arousal level in the vigilance and it could also get a high recognition rate.Have been compared with other analysis methods,the present method could more effectively recognize low arousal level and provide better technical support for wake-up mechanism of low arousal state.

关 键 词：脑电图 小波包变换 支持向量机 低觉醒状态 

分 类 号：TN911.7[电子电信—通信与信息系统] R338[电子电信—信息与通信工程]