计算机科学 ›› 2015, Vol. 42 ›› Issue (10): 25-30.
赵海,李大舟,陈星池,李思楠
ZHAO Hai, LI Da-zhou, CHEN Xing-chi and LI Si-nan
摘要： 随着可穿戴技术的快速发展,可穿戴产品中对人体生理信号分析的需求日益强烈。光电容积脉搏波技术作为一种能够体现人体心血管健康状态的重要生理信号已经开始应用到医疗、老人监护和健康监测的众多可穿戴产品之中。采用支持向量机(SVM)的分类算法,设计了一个基于光电容积脉搏波的人体窦性心率过缓检测系统。通过对光电容积脉搏波数据的采集、存储以及特征向量的提取,并利用支持向量机的分类算法,提出了一个判别用户当前心率状态是否处于窦性心率过缓的检测方法。通过实验测试,确定了分类器的最佳设置参数为C=38,g=7,此时分类准确率达94.44%,测试集验证的正确判决率达94.18%。该技术为基于光电容积脉搏波的可穿戴计算产品提供了一种新的应用领域。
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